diff --git "a/src/app.py" "b/src/app.py"
--- "a/src/app.py"
+++ "b/src/app.py"
@@ -1,1167 +1,1169 @@
-from __future__ import annotations
-
-import os
-from pathlib import Path
-from typing import List
-
-import pandas as pd
-import networkx as nx
-import streamlit as st
-import plotly.express as px
-import plotly.graph_objects as go
-from pyvis.network import Network
-import streamlit.components.v1 as components
-
-HF_REPO_ID = os.environ.get("HF_REPO_ID", "")
-HF_TOKEN   = os.environ.get("HF_TOKEN", "")
-
-st.set_page_config(page_title="CitationHub", page_icon="📚", layout="wide")
-
-ALLOWED_INTENTS = [
-    "background","uses","similarities","motivation",
-    "differences","future_work","extends",
-]
-INTENT_COLORS = {
-    "background":"#94a3b8","uses":"#22c55e","similarities":"#3b82f6",
-    "motivation":"#f59e0b","differences":"#ef4444",
-    "future_work":"#8b5cf6","extends":"#06b6d4",
-}
-NODE_COLORS = {
-    "seed_paper":"#111827","citing_paper":"#dbeafe","citation_event":"#fde68a",
-    "journal":"#ede9fe","author":"#fee2e2","affiliation":"#fae8ff",
-    "city":"#cffafe","country":"#ffedd5","field":"#e0e7ff","intent":"#dcfce7",
-}
-NODE_TYPE_COLORS = {
-    "seed_paper":"#111827","citing_paper":"#3b82f6","citation_event":"#f59e0b",
-    "journal":"#8b5cf6","author":"#ef4444","affiliation":"#ec4899",
-    "city":"#06b6d4","country":"#f97316","field":"#6366f1","intent":"#22c55e",
-}
-
-DEFAULT_DATA_DIR = Path(os.environ.get(
-    "CITATIONHUB_DATA_DIR",
-    r"C:\Users\user\OneDrive\바탕 화면\Citehub_huggingface\data",
-))
-
-
-def fmt_num(x):
-    try: return f"{int(x):,}"
-    except: return "-"
-
-
-def _hf_download(filename: str) -> str:
-    from huggingface_hub import hf_hub_download
-    return hf_hub_download(
-        repo_id=HF_REPO_ID, repo_type="dataset",
-        filename=f"data/{filename}", token=HF_TOKEN or None,
-    )
-
-
-def _read(filename: str, data_dir: Path | None = None) -> pd.DataFrame:
-    if HF_REPO_ID:
-        return pd.read_parquet(_hf_download(filename))
-    return pd.read_parquet(data_dir / filename)
-
-
-def plotly_network_fig(
-    nodes_df: pd.DataFrame,
-    edges_df: pd.DataFrame,
-    title: str = "",
-    height: int = 750,
-    seed_node_ids: list | None = None,
-) -> go.Figure:
-    """SVG 기반 Plotly 네트워크 그래프 — 확대해도 선명."""
-    G = nx.Graph()
-    node_meta: dict = {}
-    for _, row in nodes_df.iterrows():
-        nid = str(row["node_id"])
-        G.add_node(nid)
-        node_meta[nid] = row
-
-    for _, row in edges_df.iterrows():
-        s, t = str(row["source"]), str(row["target"])
-        if s in node_meta and t in node_meta:
-            G.add_edge(s, t, edge_type=row.get("edge_type", ""))
-
-    if len(G.nodes) == 0:
-        return go.Figure()
-
-    k = max(1.5, 3.0 / (len(G.nodes) ** 0.4))
-    pos = nx.spring_layout(G, seed=42, k=k, iterations=60)
-
-    # ── edges ─────────────────────────────────
-    ex, ey = [], []
-    for src, tgt in G.edges():
-        x0, y0 = pos.get(src, (0, 0))
-        x1, y1 = pos.get(tgt, (0, 0))
-        ex += [x0, x1, None]
-        ey += [y0, y1, None]
-
-    traces: list[go.BaseTraceType] = [
-        go.Scatter(
-            x=ex, y=ey, mode="lines",
-            line=dict(width=0.8, color="#cbd5e1"),
-            hoverinfo="none", showlegend=False,
-        )
-    ]
-
-    # ── nodes grouped by type ─────────────────
-    for ntype, color in NODE_TYPE_COLORS.items():
-        subset = nodes_df[nodes_df["node_type"] == ntype]
-        if subset.empty:
-            continue
-        xs, ys, hovers, texts = [], [], [], []
-        for _, row in subset.iterrows():
-            nid = str(row["node_id"])
-            if nid not in pos:
-                continue
-            x, y = pos[nid]
-            xs.append(x); ys.append(y)
-            label = str(row.get("label", ""))[:50]
-            texts.append(label if ntype == "seed_paper" else "")
-            hovers.append(
-                f"<b>{label}</b><br>"
-                f"Type: {ntype}<br>"
-                f"DOI: {row.get('doi','') or '-'}<br>"
-                f"Pub: {row.get('publication_name','') or '-'}<br>"
-                f"Group: {row.get('group','') or '-'}"
-            )
-
-        is_seed = ntype == "seed_paper"
-        traces.append(go.Scatter(
-            x=xs, y=ys,
-            mode="markers+text" if is_seed else "markers",
-            text=texts, textposition="top center",
-            hovertext=hovers, hoverinfo="text",
-            name=ntype,
-            marker=dict(
-                size=20 if is_seed else 10,
-                color=color,
-                line=dict(width=1.5 if is_seed else 0.5, color="white"),
-                symbol="circle",
-            ),
-        ))
-
-    fig = go.Figure(data=traces)
-    fig.update_layout(
-        title=dict(text=title, font=dict(size=14)),
-        showlegend=True,
-        legend=dict(title="Node type", itemsizing="constant"),
-        hovermode="closest",
-        height=height,
-        margin=dict(l=0, r=0, t=40 if title else 10, b=0),
-        paper_bgcolor="white",
-        plot_bgcolor="#f8fafc",
-        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-    )
-    return fig
-
-
-def plotly_ontology_fig(height: int = 820) -> go.Figure:
-    """CitationHub 온톨로지 구조 — Plotly SVG. 각 노드에 속성값 표시."""
-
-    # 각 노드 타입의 주요 속성 (노드 아래에 작게 표시)
-    NODE_PROPS = {
-        "seed_paper":     "doi · title · journal\nauthor · affiliation\ncountry · field · citedby_count",
-        "citation_event": "event_id · citing_year\nprimary_intent · context\nis_influential",
-        "citing_paper":   "doi · title\nyear · venue · oa_pdf",
-        "intent":         "background · uses\nsimilarities · motivation\ndifferences · future_work · extends",
-        "journal":        "journal_name",
-        "author":         "author_name · author_id",
-        "affiliation":    "affiliation_name",
-        "city":           "city_name",
-        "country":        "country_name",
-        "field":          "field_name",
-    }
-
-    node_defs = [
-        ("seed",        "Top5PctCitedPaper", "seed_paper"),
-        ("event",       "CitationEvent",     "citation_event"),
-        ("citing",      "CitingPaper",        "citing_paper"),
-        ("intent",      "Intent",             "intent"),
-        ("journal",     "Journal",            "journal"),
-        ("author",      "Author",             "author"),
-        ("affiliation", "Affiliation",        "affiliation"),
-        ("city",        "City",               "city"),
-        ("country",     "Country",            "country"),
-        ("field",       "Field",              "field"),
-    ]
-    edge_defs = [
-        ("event","citing","hasCitingPaper"),    ("event","seed","hasCitedPaper"),
-        ("event","intent","hasPrimaryIntent"),   ("seed","journal","publishedInJournal"),
-        ("seed","author","hasAuthor"),           ("seed","affiliation","hasAffiliation"),
-        ("seed","city","locatedInCity"),         ("seed","country","locatedInCountry"),
-        ("seed","field","belongsToField"),
-    ]
-    G = nx.DiGraph()
-    for nid, _, _ in node_defs:
-        G.add_node(nid)
-    for s, t, _ in edge_defs:
-        G.add_edge(s, t)
-
-    pos = nx.spring_layout(G, seed=7, k=2.5, iterations=80)
-
-    # ── 엣지 라인 + 엣지 레이블 ───────────────────────────────
-    ex, ey = [], []
-    ann = []
-    for s, t, lbl in edge_defs:
-        x0, y0 = pos[s]; x1, y1 = pos[t]
-        ex += [x0, x1, None]; ey += [y0, y1, None]
-        mx, my = (x0+x1)/2, (y0+y1)/2
-        ann.append(dict(
-            x=mx, y=my, text=f"<i>{lbl}</i>",
-            showarrow=False, font=dict(size=9, color="#64748b"),
-            bgcolor="rgba(255,255,255,0.75)",
-        ))
-
-    traces: list[go.BaseTraceType] = [
-        go.Scatter(x=ex, y=ey, mode="lines",
-                   line=dict(width=1.2, color="#94a3b8"),
-                   hoverinfo="none", showlegend=False)
-    ]
-
-    # ── 노드 + 속성 annotation ────────────────────────────────
-    for nid, label, ntype in node_defs:
-        x, y = pos[nid]
-        color = NODE_TYPE_COLORS.get(ntype, "#94a3b8")
-        props = NODE_PROPS.get(ntype, "")
-
-        # 노드 마커 + 상단 이름 레이블
-        traces.append(go.Scatter(
-            x=[x], y=[y], mode="markers+text",
-            text=[f"<b>{label}</b>"], textposition="top center",
-            hoverinfo="text",
-            hovertext=(f"<b>{label}</b><br>Type: {ntype}<br>"
-                       + props.replace("\n", "<br>")),
-            name=label, showlegend=False,
-            marker=dict(size=24, color=color,
-                        line=dict(width=1.5, color="white")),
-            textfont=dict(size=11, color="#1e293b"),
-        ))
-
-        # 속성값: 노드 아래에 작은 글씨 annotation
-        if props:
-            prop_html = props.replace("\n", "<br>")
-            ann.append(dict(
-                x=x, y=y,
-                text=f"<span style='font-size:8px;color:#64748b'>{prop_html}</span>",
-                showarrow=False,
-                xanchor="center",
-                yanchor="top",
-                yshift=-22,          # 노드 마커 아래로 오프셋
-                font=dict(size=8, color="#64748b"),
-                bgcolor="rgba(248,250,252,0.85)",
-                borderpad=2,
-            ))
-
-    fig = go.Figure(data=traces)
-    fig.update_layout(
-        showlegend=False, hovermode="closest", height=height,
-        annotations=ann,
-        margin=dict(l=10, r=10, t=20, b=10),
-        paper_bgcolor="white", plot_bgcolor="#f8fafc",
-        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-    )
-    return fig
-
-
-def inject_fullscreen(html: str) -> str:
-    extra = """
-    <button onclick="var el=document.getElementById('mynetwork');
-      if(el){if(el.requestFullscreen)el.requestFullscreen();
-      else if(el.webkitRequestFullscreen)el.webkitRequestFullscreen();}"
-      style="position:fixed;bottom:18px;right:18px;z-index:9999;
-             padding:8px 18px;background:#1e293b;color:white;border:none;
-             border-radius:8px;cursor:pointer;font-size:13px;
-             box-shadow:0 2px 8px rgba(0,0,0,0.35);">⛶ Fullscreen</button>
-    <div style="position:fixed;bottom:18px;left:18px;z-index:9999;font-size:12px;
-                color:#64748b;background:rgba(255,255,255,0.85);
-                padding:5px 10px;border-radius:6px;">
-      🖱 Scroll: zoom &nbsp;|&nbsp; Drag: pan &nbsp;|&nbsp; Click node: info</div>
-    <script>
-    // HiDPI 캔버스 해상도 보정 (Canvas 흐림 최소화)
-    (function fixDPI() {
-      var canvas = document.querySelector('#mynetwork canvas');
-      if (!canvas) { setTimeout(fixDPI, 200); return; }
-      var dpr = window.devicePixelRatio || 1;
-      if (dpr <= 1) return;
-      try {
-        if (typeof network !== 'undefined') {
-          network.canvas.pixelRatio = dpr;
-          network.redraw();
-        }
-      } catch(e) {}
-    })();
-    </script>
-    """
-    return html.replace("</body>", extra + "</body>")
-
-
-# ── 메인 데이터 로드 (11개) ────────────────────────────────────
-@st.cache_data(show_spinner=False)
-def load_data(data_dir_str: str):
-    d = None if HF_REPO_ID else Path(data_dir_str)
-
-    seed_df    = _read("seed_cited_papers_normalized.parquet", d)
-    events_df  = _read("citation_events_normalized.parquet", d)
-    citing_df  = _read("citing_papers_normalized.parquet", d)
-    authors_df      = _read("authors.parquet", d)
-    affiliations_df = _read("affiliations.parquet", d)
-    aff_geo_df      = _read("affiliation_geo.parquet", d)
-    cities_df       = _read("cities.parquet", d)
-    countries_df    = _read("countries.parquet", d)
-    fields_df       = _read("fields.parquet", d)
-    intents_df      = _read("intents.parquet", d)
-    journals_df     = _read("journals.parquet", d)
-
-    seed = pd.DataFrame({
-        "seed_paper_id":  seed_df["seed_paper_id"],
-        "doi":            seed_df.get("doi", pd.Series(dtype=str)).fillna(""),
-        "title":          seed_df.get("title", pd.Series(dtype=str)).fillna(""),
-        "journal":        seed_df.get("publication_name", pd.Series(dtype=str)).fillna(""),
-        "author":         seed_df.get("creator", pd.Series(dtype=str)).fillna(""),
-        "affiliation":    seed_df.get("affilname", pd.Series(dtype=str)).fillna(""),
-        "city":           seed_df.get("affiliation_city", pd.Series(dtype=str)).fillna(""),
-        "country":        seed_df.get("affiliation_country", pd.Series(dtype=str)).fillna(""),
-        "field":          seed_df.get("group", pd.Series(dtype=str)).fillna(""),
-        "citedby_count":  pd.to_numeric(seed_df.get("citedby_count"), errors="coerce").fillna(0).astype(int),
-        "author_id":      seed_df.get("author_id", pd.Series(dtype=object)),
-        "affiliation_id": seed_df.get("affiliation_id", pd.Series(dtype=object)),
-        "country_id":     seed_df.get("country_id", pd.Series(dtype=object)),
-        "field_id":       seed_df.get("field_id", pd.Series(dtype=object)),
-        "journal_id":     seed_df.get("journal_id", pd.Series(dtype=object)),
-    })
-    for col in ["title","doi","journal","field","country"]:
-        seed[f"{col}_lc"] = seed[col].astype(str).str.lower()
-    seed = seed.sort_values(["citedby_count","title"], ascending=[False,True]).reset_index(drop=True)
-
-    events = pd.DataFrame({
-        "citation_event_id": events_df["citation_event_id"],
-        "seed_paper_id":     events_df["cited_seed_paper_id"],
-        "citing_paper_id":   events_df["citing_paper_id"],
-        "citing_title":      events_df.get("citing_title", pd.Series(dtype=str)).fillna(""),
-        "citing_doi":        events_df.get("citing_doi", pd.Series(dtype=str)).fillna(""),
-        "citing_year":       pd.to_numeric(events_df.get("citing_year"), errors="coerce"),
-        "citing_venue":      events_df.get("citing_venue", pd.Series(dtype=str)).fillna(""),
-        "primary_intent":    events_df.get("primary_intent", pd.Series(dtype=str)).fillna(""),
-        "contexts":          events_df.get("contexts"),
-        "context_count":     pd.to_numeric(events_df.get("context_count"), errors="coerce").fillna(0).astype(int),
-        "intent_count":      pd.to_numeric(events_df.get("intent_count"), errors="coerce").fillna(0).astype(int),
-        "is_influential":    events_df.get("is_influential", pd.Series(dtype=bool)).fillna(False),
-        "field_id":          events_df.get("field_id", pd.Series(dtype=object)),
-    })
-    events = events[events["primary_intent"].isin(ALLOWED_INTENTS)].reset_index(drop=True)
-
-    citing = pd.DataFrame({
-        "citing_paper_id": citing_df["citing_paper_id"],
-        "doi":    citing_df.get("doi",   pd.Series(dtype=str)).fillna(""),
-        "title":  citing_df.get("title", pd.Series(dtype=str)).fillna(""),
-        "year":   pd.to_numeric(citing_df.get("year"), errors="coerce"),
-        "venue":  citing_df.get("venue", pd.Series(dtype=str)).fillna(""),
-        "oa_pdf": citing_df.get("oa_pdf",pd.Series(dtype=str)).fillna(""),
-    })
-
-    filters = {
-        "fields":    sorted([x for x in seed["field"].dropna().astype(str).unique() if x]),
-        "countries": sorted([x for x in seed["country"].dropna().astype(str).unique() if x]),
-        "journals":  sorted([x for x in seed["journal"].dropna().astype(str).unique() if x]),
-        "intents":   ALLOWED_INTENTS,
-        "year_min":  int(events["citing_year"].dropna().min()) if events["citing_year"].notna().any() else 2000,
-        "year_max":  int(events["citing_year"].dropna().max()) if events["citing_year"].notna().any() else 2025,
-    }
-    overview = {
-        "seed_papers":     int(len(seed)),
-        "citation_events": int(len(events)),
-        "citing_papers":   int(events["citing_paper_id"].nunique()),
-        "authors":         int(len(authors_df)),
-        "journals":        int(seed["journal"].replace("", pd.NA).dropna().nunique()),
-        "countries":       int(seed["country"].replace("", pd.NA).dropna().nunique()),
-        "fields":          int(seed["field"].replace("", pd.NA).dropna().nunique()),
-        "intents":         len(ALLOWED_INTENTS),
-    }
-    return (seed, events, citing, filters, overview,
-            authors_df, affiliations_df, aff_geo_df,
-            cities_df, countries_df, fields_df, intents_df, journals_df)
-
-
-# ── KG 데이터: DuckDB 방식으로 분리 로드 ─────────────────────
-#   kg_nodes  : pandas 전체 로드 (~160MB 파일, 메모리 허용 범위)
-#   kg_edges  : DuckDB로 필요한 노드의 엣지만 쿼리 (전체 로드 안 함)
-#   enriched  : DuckDB로 집계 통계만 쿼리 (전체 로드 안 함)
-
-@st.cache_data(show_spinner=False)
-def load_kg_nodes(data_dir_str: str) -> pd.DataFrame:
-    """kg_nodes 전체 로드 (3.4M rows, ~160MB 파일)"""
-    d = None if HF_REPO_ID else Path(data_dir_str)
-    return _read("kg_nodes.parquet", d)
-
-
-@st.cache_data(show_spinner=False)
-def get_parquet_path(filename: str, data_dir_str: str) -> str:
-    """파일 경로 반환 (HF면 로컬 캐시에 다운로드 후 경로 반환)"""
-    if HF_REPO_ID:
-        return _hf_download(filename)
-    # DuckDB용: 역슬래시 → 슬래시 변환
-    return str(Path(data_dir_str) / filename).replace("\\", "/")
-
-
-@st.cache_data(show_spinner=False)
-def query_kg_edges_for_node(node_id: str, kg_edges_path: str, max_edges: int = 80) -> pd.DataFrame:
-    """DuckDB: 특정 노드의 엣지만 parquet에서 바로 쿼리 (전체 로드 없음)"""
-    import duckdb
-    safe_path = kg_edges_path.replace("\\", "/")
-    safe_node = node_id.replace("'", "''")
-    q = f"""
-    SELECT source, target, edge_type
-    FROM read_parquet('{safe_path}')
-    WHERE source = '{safe_node}' OR target = '{safe_node}'
-    LIMIT {int(max_edges)}
-    """
-    return duckdb.execute(q).df()
-
-
-@st.cache_data(show_spinner=False)
-def query_enriched_stats(enriched_path: str):
-    """DuckDB: enriched 전체 로드 없이 집계 통계만 쿼리"""
-    import duckdb
-    safe_path = enriched_path.replace("\\", "/")
-
-    sem_df = duckdb.execute(f"""
-        SELECT has_semantic_evidence, COUNT(*) AS count
-        FROM read_parquet('{safe_path}')
-        GROUP BY has_semantic_evidence
-    """).df()
-
-    field_df = duckdb.execute(f"""
-        SELECT field_folder AS field,
-               AVG(CAST(has_semantic_evidence AS INTEGER)) AS sem_ratio,
-               COUNT(*) AS event_count
-        FROM read_parquet('{safe_path}')
-        GROUP BY field_folder
-        ORDER BY sem_ratio DESC
-        LIMIT 20
-    """).df()
-
-    return sem_df, field_df
-
-
-@st.cache_data(show_spinner=False)
-def query_explorer_edges(node_id: str, kg_edges_path: str, max_edges: int = 60) -> pd.DataFrame:
-    """DuckDB: KG Explorer용 임의 노드 엣지 쿼리"""
-    import duckdb
-    safe_path = kg_edges_path.replace("\\", "/")
-    safe_node = node_id.replace("'", "''")
-    q = f"""
-    SELECT source, target, edge_type
-    FROM read_parquet('{safe_path}')
-    WHERE source = '{safe_node}' OR target = '{safe_node}'
-    LIMIT {int(max_edges)}
-    """
-    return duckdb.execute(q).df()
-
-
-# ── 헬퍼 ───────────────────────────────────────────────────────
-def filter_seed_papers(seed, q, fields, countries, journals):
-    df = seed.copy()
-    q = (q or "").strip().lower()
-    if q:
-        df = df[df["title_lc"].str.contains(q, na=False) | df["doi_lc"].str.contains(q, na=False)]
-    if fields:    df = df[df["field"].str.lower().isin({x.lower() for x in fields})]
-    if countries: df = df[df["country"].str.lower().isin({x.lower() for x in countries})]
-    if journals:  df = df[df["journal"].str.lower().isin({x.lower() for x in journals})]
-    return df.reset_index(drop=True)
-
-
-def event_subset(events, seed_paper_id, year_min, year_max):
-    df = events[events["seed_paper_id"] == seed_paper_id].copy()
-    df = df[df["citing_year"].fillna(-99999) >= year_min]
-    df = df[df["citing_year"].fillna(99999) <= year_max]
-    return df.reset_index(drop=True)
-
-
-def build_intent_summary(df):
-    counts = df.groupby("primary_intent").size().to_dict()
-    return pd.DataFrame({"intent": ALLOWED_INTENTS,
-                          "count": [int(counts.get(i,0)) for i in ALLOWED_INTENTS]})
-
-
-def build_context_rows(df, limit=20):
-    rows = []
-    df = df.sort_values(["context_count","intent_count","citing_year"],
-                        ascending=[False,False,False], na_position="last")
-    for _, row in df.iterrows():
-        ctx = row["contexts"]
-        if isinstance(ctx, list) and ctx:
-            for c in ctx[:2]:
-                rows.append({"primary_intent": row["primary_intent"],
-                             "citing_title": row["citing_title"],
-                             "citing_doi": row["citing_doi"],
-                             "citing_year": None if pd.isna(row["citing_year"]) else int(row["citing_year"]),
-                             "context": c})
-        if len(rows) >= limit: break
-    return pd.DataFrame(rows[:limit])
-
-
-def build_citing_table(df, limit=30):
-    if df.empty:
-        return pd.DataFrame(columns=["citing_title","citing_year","primary_intent","context_count"])
-    return (df.sort_values(["context_count","intent_count","citing_year"],
-                            ascending=[False,False,False], na_position="last")
-            [["citing_paper_id","citing_title","citing_doi","citing_year","primary_intent","context_count"]]
-            .drop_duplicates(subset=["citing_paper_id"]).head(limit))
-
-
-def get_cocited_papers(selected_seed_id, events, seed, top_n=15):
-    """선택된 seed paper를 인용한 논문들이 함께 인용한 다른 seed papers"""
-    citing_ids = events[events["seed_paper_id"] == selected_seed_id]["citing_paper_id"].unique()
-    cocited = (events[events["citing_paper_id"].isin(citing_ids) &
-                      (events["seed_paper_id"] != selected_seed_id)]
-               .groupby("seed_paper_id").size()
-               .reset_index(name="co_citation_count")
-               .sort_values("co_citation_count", ascending=False)
-               .head(top_n))
-    return cocited.merge(seed[["seed_paper_id","title","field","journal","citedby_count"]],
-                         on="seed_paper_id", how="left")
-
-
-def get_kg_subgraph(seed_doi: str, kg_nodes, kg_edges, max_edges=80):
-    """선택된 seed paper의 KG 1-hop 서브그래프 반환"""
-    node_id = f"seed:{seed_doi}"
-    edges = kg_edges[(kg_edges["source"] == node_id) |
-                     (kg_edges["target"] == node_id)].head(max_edges)
-    if edges.empty:
-        return None, None
-    all_node_ids = set(edges["source"].tolist()) | set(edges["target"].tolist())
-    nodes = kg_nodes[kg_nodes["node_id"].isin(all_node_ids)]
-    return nodes, edges
-
-
-def get_explorer_subgraph(search_node_id: str, kg_nodes, kg_edges, max_edges=60):
-    """KG Explorer: 임의 노드 기준 서브그래프"""
-    edges = kg_edges[(kg_edges["source"] == search_node_id) |
-                     (kg_edges["target"] == search_node_id)].head(max_edges)
-    if edges.empty:
-        return None, None
-    all_ids = set(edges["source"].tolist()) | set(edges["target"].tolist())
-    nodes = kg_nodes[kg_nodes["node_id"].isin(all_ids)]
-    return nodes, edges
-
-
-# ── pyvis 빌더 ─────────────────────────────────────────────────
-def pyvis_citation_graph(seed_row, events_df):
-    net = Network(height="780px", width="100%", bgcolor="#ffffff", font_color="#111827", directed=True)
-    sid = seed_row["seed_paper_id"]
-    net.add_node(sid, label=seed_row["title"][:60], color="#111827", size=34, shape="dot",
-                 font={"color":"white"})
-    for _, row in events_df.sort_values(["context_count","intent_count"],
-                                         ascending=False).head(40).iterrows():
-        cid = row["citing_paper_id"]
-        net.add_node(cid, label=(row["citing_title"] or row["citing_doi"] or cid)[:60],
-                     color=NODE_COLORS["citing_paper"], size=18, shape="dot")
-        ctx = (row["contexts"] or [])[0] if isinstance(row["contexts"], list) and row["contexts"] else ""
-        yr  = "" if pd.isna(row["citing_year"]) else int(row["citing_year"])
-        net.add_edge(cid, sid, label=row["primary_intent"],
-                     color=INTENT_COLORS.get(row["primary_intent"],"#94a3b8"),
-                     title=f"Intent: {row['primary_intent']}<br>Year: {yr}<br>{ctx}")
-    net.barnes_hut()
-    return inject_fullscreen(net.generate_html())
-
-
-def pyvis_ontology():
-    net = Network(height="780px", width="100%", bgcolor="#ffffff", font_color="#111827", directed=True)
-    for nid, label, typ in [
-        ("seed","Top5PctCitedPaper","seed_paper"),("event","CitationEvent","citation_event"),
-        ("citing","CitingPaper","citing_paper"),  ("intent","Intent","intent"),
-        ("journal","Journal","journal"),           ("author","Author","author"),
-        ("affiliation","Affiliation","affiliation"),("city","City","city"),
-        ("country","Country","country"),           ("field","Field","field"),
-    ]:
-        net.add_node(nid, label=label, color=NODE_COLORS[typ], size=24)
-    for s, t, l in [
-        ("event","citing","hasCitingPaper"),("event","seed","hasCitedPaper"),
-        ("event","intent","hasPrimaryIntent"),("seed","journal","publishedInJournal"),
-        ("seed","author","hasAuthor"),        ("seed","affiliation","hasAffiliation"),
-        ("seed","city","locatedInCity"),      ("seed","country","locatedInCountry"),
-        ("seed","field","belongsToField"),
-    ]:
-        net.add_edge(s, t, label=l)
-    net.barnes_hut()
-    return inject_fullscreen(net.generate_html())
-
-
-def pyvis_from_kg(nodes_df, edges_df, height="780px"):
-    """kg_nodes / kg_edges DataFrame으로 pyvis 그래프 생성"""
-    net = Network(height=height, width="100%", bgcolor="#ffffff", font_color="#111827", directed=True)
-    for _, row in nodes_df.iterrows():
-        ntype = row.get("node_type","")
-        color = NODE_TYPE_COLORS.get(ntype,"#94a3b8")
-        label = str(row.get("label",""))[:55]
-        size  = 30 if ntype == "seed_paper" else 16
-        font  = {"color":"white"} if ntype == "seed_paper" else {}
-        tooltip = f"Type: {ntype}<br>DOI: {row.get('doi','')}<br>Pub: {row.get('publication_name','')}"
-        net.add_node(str(row["node_id"]), label=label, color=color,
-                     size=size, shape="dot", title=tooltip, font=font)
-    for _, row in edges_df.iterrows():
-        net.add_edge(str(row["source"]), str(row["target"]),
-                     label=row.get("edge_type",""), color="#94a3b8")
-    net.barnes_hut()
-    return inject_fullscreen(net.generate_html())
-
-
-# ═══════════════════════════════════════════════════════════════
-#  메인 UI
-# ═══════════════════════════════════════════════════════════════
-st.title("CitationHub")
-st.caption("Explore influential papers (top 5% cited), their citation networks, and knowledge graphs.")
-
-# ── Sidebar ────────────────────────────────────────────────────
-with st.sidebar:
-    st.subheader("Data source")
-    if HF_REPO_ID:
-        data_dir_val = "hf"
-        st.caption(f"Hugging Face: {HF_REPO_ID}")
-    else:
-        data_dir_val = st.text_input("Parquet directory", str(DEFAULT_DATA_DIR))
-
-    try:
-        (seed, events, citing, filters, overview,
-         authors_df, affiliations_df, aff_geo_df,
-         cities_df, countries_df, fields_df, intents_df, journals_df) = load_data(data_dir_val)
-        st.success("Data loaded")
-    except Exception as e:
-        st.error(str(e)); st.stop()
-
-    st.subheader("Search seed papers")
-    q_input = st.text_input("Title or DOI")
-    if "q_submit" not in st.session_state: st.session_state["q_submit"] = ""
-    if st.button("Search", use_container_width=True):
-        st.session_state["q_submit"] = q_input
-
-    fields_sel    = st.multiselect("Field", filters["fields"])
-    countries_sel = st.multiselect("Country", filters["countries"])
-    journals_sel  = st.multiselect("Journal", filters["journals"][:200])
-    y_min = max(2000, filters["year_min"])
-    year_min, year_max = st.slider("Citing year", y_min, filters["year_max"], (y_min, filters["year_max"]))
-
-    seed_filtered = filter_seed_papers(seed, st.session_state["q_submit"],
-                                       fields_sel, countries_sel, journals_sel)
-
-    st.subheader("Overview counts")
-    c1, c2 = st.columns(2)
-    c1.metric("Seed papers",     fmt_num(overview["seed_papers"]))
-    c2.metric("Citation events", fmt_num(overview["citation_events"]))
-    c1.metric("Citing papers",   fmt_num(overview["citing_papers"]))
-    c2.metric("Authors",         fmt_num(overview["authors"]))
-    c1.metric("Countries",       fmt_num(overview["countries"]))
-    c2.metric("Fields",          fmt_num(overview["fields"]))
-
-    options = seed_filtered["seed_paper_id"].tolist()
-    if not options:
-        st.warning("No seed papers match the current search."); st.stop()
-    current     = st.session_state.get("selected_seed_id", options[0])
-    default_idx = options.index(current) if current in options else 0
-    selected_seed_id = st.selectbox(
-        "Seed paper", options, index=default_idx,
-        format_func=lambda sid: seed_filtered.loc[
-            seed_filtered["seed_paper_id"]==sid, "title"].iloc[0],
-    )
-    st.session_state["selected_seed_id"] = selected_seed_id
-
-selected_seed  = seed_filtered[seed_filtered["seed_paper_id"]==selected_seed_id].iloc[0]
-seed_events    = event_subset(events, selected_seed_id, year_min, year_max)
-intent_summary = build_intent_summary(seed_events)
-contexts_df    = build_context_rows(seed_events)
-citing_table   = build_citing_table(seed_events)
-
-# ── 탭 ─────────────────────────────────────────────────────────
-(tab_overview, tab_cnet, tab_ontology,
- tab_kg_exp, tab_geo, tab_analytics) = st.tabs([
-    "Overview","Citation Network","Ontology",
-    "Knowledge Graph","Geographic Map","Analytics",
-])
-
-
-# ═══ 1. OVERVIEW ═══════════════════════════════════════════════
-with tab_overview:
-    col1, col2 = st.columns(2)
-    with col1:
-        st.subheader("Seed paper detail")
-        dc1, dc2 = st.columns(2)
-        dc1.metric("Cited by",        fmt_num(selected_seed["citedby_count"]))
-        dc2.metric("Citation events", fmt_num(len(seed_events)))
-        for label, key in [
-            ("Title","title"),("DOI","doi"),("Journal","journal"),
-            ("Author","author"),("Affiliation","affiliation"),
-            ("City","city"),("Country","country"),("Field","field"),
-        ]:
-            st.markdown(f"**{label}**  \n{selected_seed[key] or '-'}")
-
-        st.subheader("Related citing papers")
-        st.dataframe(citing_table.rename(columns={
-            "citing_title":"Title","citing_year":"Year",
-            "primary_intent":"Intent","context_count":"Contexts"}),
-            use_container_width=True, hide_index=True)
-
-        st.subheader("Co-cited seed papers")
-        st.caption("같은 citing paper에 의해 함께 인용된 다른 top 5% 논문들")
-        cocited = get_cocited_papers(selected_seed_id, events, seed)
-        if cocited.empty:
-            st.info("Co-cited papers not found.")
-        else:
-            st.dataframe(cocited.rename(columns={
-                "co_citation_count":"Co-citations","title":"Title",
-                "field":"Field","citedby_count":"Cited by"}),
-                use_container_width=True, hide_index=True)
-
-    with col2:
-        st.subheader("Intent distribution (selected paper)")
-        fig = px.bar(intent_summary, x="intent", y="count", color="intent",
-                     color_discrete_map=INTENT_COLORS)
-        fig.update_layout(showlegend=False, xaxis_title="", yaxis_title="Count")
-        st.plotly_chart(fig, use_container_width=True)
-
-        st.subheader("CitationHub Intent Distribution")
-        all_intents = events.groupby("primary_intent").size().to_dict()
-        ai_df = pd.DataFrame({"intent": ALLOWED_INTENTS,
-                               "count": [int(all_intents.get(i, 0)) for i in ALLOWED_INTENTS]})
-        fig2 = px.bar(ai_df, x="intent", y="count", color="intent",
-                      color_discrete_map=INTENT_COLORS)
-        fig2.update_layout(showlegend=False, xaxis_title="", yaxis_title="Count")
-        st.plotly_chart(fig2, use_container_width=True)
-
-        st.subheader("CitationHub Field Distribution")
-        fd = (seed_filtered.groupby("field", dropna=False).size()
-              .reset_index(name="count").sort_values("count", ascending=False).head(20))
-        fd["field"] = fd["field"].replace("","Unknown")
-        st.plotly_chart(
-            px.bar(fd, x="field", y="count").update_layout(xaxis_title="", yaxis_title="Count"),
-            use_container_width=True)
-
-    st.subheader("Citation contexts")
-    if contexts_df.empty:
-        st.info("No contexts available.")
-    else:
-        for _, row in contexts_df.iterrows():
-            st.markdown(
-                f"""<div style="border:1px solid #e2e8f0;border-radius:14px;padding:12px;
-                margin-bottom:10px;background:#f8fafc;">
-                <div style="display:inline-block;background:{INTENT_COLORS.get(row['primary_intent'],'#64748b')};
-                color:white;border-radius:999px;padding:4px 8px;font-size:12px;margin-bottom:6px;">
-                {row['primary_intent']}</div>
-                <div style="font-size:12px;color:#64748b;margin-bottom:6px;">
-                {row['citing_year'] or '-'} · {row['citing_title'] or row['citing_doi']}</div>
-                <div>{row['context']}</div></div>""",
-                unsafe_allow_html=True)
-
-
-# ═══ 2. CITATION NETWORK ════════════════════════════════════════
-with tab_cnet:
-    st.subheader("Citation Network")
-    st.caption("🖱 Scroll: zoom  |  Drag: pan  |  Click node: info  |  ⛶ button: fullscreen")
-    if seed_events.empty:
-        st.info("No citation network data for this seed paper.")
-    else:
-        components.html(pyvis_citation_graph(selected_seed, seed_events), height=820, scrolling=True)
-
-
-# ═══ 3. ONTOLOGY ════════════════════════════════════════════════
-with tab_ontology:
-    st.subheader("CitationHub Ontology")
-    st.plotly_chart(plotly_ontology_fig(height=750), use_container_width=True)
-
-
-# ═══ 4. KNOWLEDGE GRAPH (KG Explorer) ═══════════════════════════
-with tab_kg_exp:
-    st.subheader("KG Explorer")
-
-    try:
-        with st.spinner("Loading..."):
-            kg_nodes_exp  = load_kg_nodes(data_dir_val)
-            kg_edges_path = get_parquet_path("kg_edges.parquet", data_dir_val)
-
-        # ── 노드/엣지 타입 분포 통계
-        import duckdb as _ddb
-
-        nt = kg_nodes_exp["node_type"].value_counts().reset_index()
-        nt.columns = ["node_type", "count"]
-
-        et = _ddb.execute(f"""
-            SELECT edge_type, COUNT(*) AS count
-            FROM read_parquet('{kg_edges_path}')
-            GROUP BY edge_type ORDER BY count DESC
-        """).df()
-
-        col_a, col_b, col_c, col_d = st.columns([1, 2, 1, 2])
-        with col_a:
-            st.subheader("Node Types")
-            st.dataframe(nt, use_container_width=True, hide_index=True)
-        with col_b:
-            st.subheader("CitationHub KG Node Distribution")
-            nt_fig = px.bar(nt, x="node_type", y="count", color="node_type",
-                            color_discrete_map=NODE_TYPE_COLORS)
-            nt_fig.update_layout(showlegend=False, xaxis_title="", yaxis_title="Count")
-            st.plotly_chart(nt_fig, use_container_width=True)
-        with col_c:
-            st.subheader("Edge Types")
-            st.dataframe(et, use_container_width=True, hide_index=True)
-        with col_d:
-            st.subheader("CitationHub KG Edge Distribution")
-            et_fig = px.bar(et, x="edge_type", y="count", color="edge_type")
-            et_fig.update_layout(showlegend=False, xaxis_title="",
-                                 yaxis_title="Count", xaxis_tickangle=-35)
-            st.plotly_chart(et_fig, use_container_width=True)
-
-        # ── Multi-Node Knowledge Graph (2-hop: 10 node types + 10 edge types)
-        st.markdown("---")
-        st.subheader("Multi-Node Knowledge Graph")
-        st.caption("🖱 Scroll: zoom  |  Drag: pan  |  Click node: info  |  ⛶ button: fullscreen")
-
-        n_seeds = st.slider("Number of seed papers", 3, 15, 6, key="kg_exp_n_seeds")
-
-        # edge type당 고정 샘플 수 — 10 types × 10 = 최대 100 edges
-        EDGES_PER_TYPE = 10
-
-        with st.spinner("Querying graph..."):
-            top_seeds = (kg_nodes_exp[kg_nodes_exp["node_type"] == "seed_paper"]
-                         .sort_values("citedby_count", ascending=False)
-                         .head(n_seeds))
-            seed_ids = top_seeds["node_id"].tolist()
-
-            if seed_ids:
-                ids_sql = ", ".join(f"'{sid}'" for sid in seed_ids)
-
-                # 1-hop: seed paper에 연결된 모든 edge type
-                # → journal / author / affiliation / city / country / field / citation_event
-                hop1 = _ddb.execute(f"""
-                    WITH ranked AS (
-                        SELECT source, target, edge_type,
-                               ROW_NUMBER() OVER (
-                                   PARTITION BY edge_type ORDER BY source
-                               ) AS rn
-                        FROM read_parquet('{kg_edges_path}')
-                        WHERE source IN ({ids_sql}) OR target IN ({ids_sql})
-                    )
-                    SELECT source, target, edge_type FROM ranked
-                    WHERE rn <= {EDGES_PER_TYPE}
-                """).df()
-
-                # 2-hop: kg_nodes_exp 타입 기반으로 citation_event 노드 ID 추출
-                # (prefix 가정 없이 실제 node_type 컬럼으로 확인)
-                hop1_all_ids = set(hop1["source"].tolist()) | set(hop1["target"].tolist())
-                event_node_ids = (
-                    kg_nodes_exp[
-                        kg_nodes_exp["node_id"].isin(hop1_all_ids) &
-                        (kg_nodes_exp["node_type"] == "citation_event")
-                    ]["node_id"].tolist()[:40]
-                )
-
-                if event_node_ids:
-                    ev_sql = ", ".join(f"'{eid}'" for eid in event_node_ids)
-                    # citation_event → HAS_CITING_PAPER → citing_paper
-                    # citation_event → HAS_PRIMARY_INTENT → intent
-                    hop2 = _ddb.execute(f"""
-                        WITH ranked AS (
-                            SELECT source, target, edge_type,
-                                   ROW_NUMBER() OVER (
-                                       PARTITION BY edge_type ORDER BY source
-                                   ) AS rn
-                            FROM read_parquet('{kg_edges_path}')
-                            WHERE (source IN ({ev_sql}) OR target IN ({ev_sql}))
-                              AND edge_type NOT IN (
-                                  SELECT DISTINCT edge_type
-                                  FROM read_parquet('{kg_edges_path}')
-                                  WHERE source IN ({ids_sql}) OR target IN ({ids_sql})
-                              )
-                        )
-                        SELECT source, target, edge_type FROM ranked
-                        WHERE rn <= {EDGES_PER_TYPE}
-                    """).df()
-                    exp_edges = pd.concat([hop1, hop2]).drop_duplicates(
-                        subset=["source", "target", "edge_type"]
-                    )
-                else:
-                    exp_edges = hop1
-
-                all_exp_ids = set(exp_edges["source"].tolist()) | set(exp_edges["target"].tolist())
-                exp_nodes = kg_nodes_exp[kg_nodes_exp["node_id"].isin(all_exp_ids)]
-
-                c1, c2, c3, c4 = st.columns(4)
-                c1.metric("Nodes",      fmt_num(len(exp_nodes)))
-                c2.metric("Edges",      fmt_num(len(exp_edges)))
-                c3.metric("Node types", fmt_num(exp_nodes["node_type"].nunique()))
-                c4.metric("Edge types", fmt_num(exp_edges["edge_type"].nunique()))
-
-                kg_html = pyvis_from_kg(exp_nodes, exp_edges)
-                components.html(kg_html, height=860, scrolling=True)
-
-    except Exception as e:
-        st.error(str(e))
-
-
-# ═══ 6. GEOGRAPHIC MAP ══════════════════════════════════════════
-with tab_geo:
-    st.subheader("Geographic Distribution of Seed Papers")
-
-    country_cnt = (seed_filtered.groupby("country", dropna=False).size()
-                   .reset_index(name="count").rename(columns={"country":"country_name"}))
-    country_cnt = country_cnt[country_cnt["country_name"].str.strip() != ""]
-
-    if not country_cnt.empty:
-        fig_map = px.choropleth(country_cnt, locations="country_name",
-                                locationmode="country names", color="count",
-                                hover_name="country_name",
-                                color_continuous_scale="Blues",
-                                title="Seed Papers by Country")
-        fig_map.update_layout(geo=dict(showframe=False), height=500)
-        st.plotly_chart(fig_map, use_container_width=True)
-
-    st.subheader("Top Cities")
-    city_cnt = (seed_filtered.merge(
-                    aff_geo_df[["affiliation_name","city_name","country_name"]],
-                    left_on="affiliation", right_on="affiliation_name", how="left")
-                .groupby(["country_name","city_name"], dropna=False).size()
-                .reset_index(name="count").dropna(subset=["country_name"])
-                .sort_values("count", ascending=False).head(30))
-    if not city_cnt.empty:
-        st.plotly_chart(
-            px.bar(city_cnt, x="city_name", y="count", color="country_name",
-                   title="Top 30 Cities")
-            .update_layout(xaxis_title="", yaxis_title="# Seed Papers", xaxis_tickangle=-40),
-            use_container_width=True)
-
-    # ── Affiliation 시각화 ──────────────────────────────────────
-    st.subheader("Top Affiliations")
-    geo_col1, geo_col2 = st.columns(2)
-
-    with geo_col1:
-        aff_cnt = (seed_filtered[seed_filtered["affiliation"].str.strip() != ""]
-                   .groupby("affiliation").size()
-                   .reset_index(name="count")
-                   .sort_values("count", ascending=False).head(20))
-        if not aff_cnt.empty:
-            st.plotly_chart(
-                px.bar(aff_cnt, x="count", y="affiliation", orientation="h",
-                       title="Top 20 Affiliations by Seed Papers",
-                       labels={"count": "Seed Papers", "affiliation": ""})
-                .update_layout(yaxis=dict(autorange="reversed"),
-                               xaxis_title="Seed Papers", yaxis_title="", height=520),
-                use_container_width=True)
-
-    with geo_col2:
-        aff_country = (seed_filtered[
-                (seed_filtered["affiliation"].str.strip() != "") &
-                (seed_filtered["country"].str.strip() != "")
-            ]
-            .groupby(["country", "affiliation"]).size()
-            .reset_index(name="count")
-            .sort_values("count", ascending=False)
-        )
-        top_affs = aff_country.groupby("affiliation")["count"].sum().nlargest(20).index
-        aff_country_top = aff_country[aff_country["affiliation"].isin(top_affs)]
-        if not aff_country_top.empty:
-            st.plotly_chart(
-                px.bar(aff_country_top, x="count", y="affiliation",
-                       color="country", orientation="h",
-                       title="Top Affiliations by Country",
-                       labels={"count": "Seed Papers", "affiliation": "", "country": "Country"})
-                .update_layout(yaxis=dict(autorange="reversed"),
-                               barmode="stack",
-                               xaxis_title="Seed Papers", yaxis_title="",
-                               legend_title="Country", height=520),
-                use_container_width=True)
-
-
-# ═══ 7. ANALYTICS ═══════════════════════════════════════════════
-with tab_analytics:
-    col_a, col_b = st.columns(2)
-
-    with col_a:
-        st.subheader("Top Authors")
-        if "author_id" in seed.columns and not seed["author_id"].isna().all():
-            top_auth = (seed.explode("author_id")
-                        .merge(authors_df, on="author_id", how="left")
-                        .groupby("author_name").size()
-                        .reset_index(name="paper_count")
-                        .sort_values("paper_count", ascending=False).head(20))
-        else:
-            top_auth = (seed["author"].value_counts()
-                        .reset_index().rename(columns={"author":"author_name","count":"paper_count"})
-                        .head(20))
-        top_auth = top_auth[top_auth["author_name"].str.strip() != ""]
-        st.plotly_chart(
-            px.bar(top_auth, x="paper_count", y="author_name", orientation="h",
-                   title="Top 20 Authors")
-            .update_layout(yaxis=dict(autorange="reversed"),
-                           xaxis_title="Seed Papers", yaxis_title=""),
-            use_container_width=True)
-
-    with col_b:
-        st.subheader("Top Journals")
-        top_jnl = (seed.groupby("journal").size()
-                   .reset_index(name="count").sort_values("count", ascending=False).head(20))
-        top_jnl = top_jnl[top_jnl["journal"].str.strip() != ""]
-        st.plotly_chart(
-            px.bar(top_jnl, x="count", y="journal", orientation="h",
-                   title="Top 20 Journals")
-            .update_layout(yaxis=dict(autorange="reversed"),
-                           xaxis_title="Seed Papers", yaxis_title=""),
-            use_container_width=True)
-
-    st.markdown("---")
-    col_c, col_d = st.columns(2)
-
-    with col_c:
-        st.subheader("CitationHub Field × Intent Distribution Heatmap")
-        fi = (seed[["seed_paper_id","field"]]
-              .merge(events[["seed_paper_id","primary_intent"]], on="seed_paper_id", how="inner")
-              .groupby(["field","primary_intent"]).size().reset_index(name="count"))
-        if not fi.empty:
-            pivot = fi.pivot(index="field", columns="primary_intent", values="count").fillna(0)
-            st.plotly_chart(
-                px.imshow(pivot, color_continuous_scale="Blues",
-                          title="CitationHub Field × Intent Distribution Heatmap",
-                          aspect="auto")
-                .update_layout(xaxis_title="Intent", yaxis_title="Field"),
-                use_container_width=True)
-
-    with col_d:
-        st.subheader("Influential Citations (selected paper)")
-        if "is_influential" in seed_events.columns:
-            inf = seed_events["is_influential"].value_counts().reset_index()
-            inf.columns = ["is_influential","count"]
-            inf["label"] = inf["is_influential"].map({True:"Influential", False:"Non-influential"})
-            st.plotly_chart(
-                px.pie(inf, names="label", values="count",
-                       title="Influential vs Non-influential"),
-                use_container_width=True)
-
-    # ── Intent Evolution over Years ────────────────────────────
-    st.markdown("---")
-    st.subheader("CitationHub Intent Evolution over Years")
-    st.caption("How citation intents have changed across all papers over time")
-    intent_trend_raw = (
-        events.dropna(subset=["citing_year"])
-        .assign(year=lambda df: df["citing_year"].astype(int))
-        .query("year >= 2000")
-        .groupby(["year", "primary_intent"]).size()
-        .reset_index(name="count")
-    )
-    if not intent_trend_raw.empty:
-        st.plotly_chart(
-            px.area(
-                intent_trend_raw, x="year", y="count", color="primary_intent",
-                color_discrete_map=INTENT_COLORS,
-                labels={"primary_intent": "Intent", "count": "Citations", "year": "Year"},
-            ).update_layout(
-                legend_title="Intent",
-                xaxis_title="Year", yaxis_title="# Citations",
-                hovermode="x unified",
-            ),
-            use_container_width=True,
-        )
-
-    # ── Top Citing Venues ───────────────────────────────────────
-    st.markdown("---")
-    col_v1, col_v2 = st.columns(2)
-
-    with col_v1:
-        st.subheader("Top Citing Venues")
-        st.caption("Journals/conferences that cite seed papers most")
-        venue_cnt = (
-            events[events["citing_venue"].str.strip() != ""]
-            .groupby("citing_venue").size()
-            .reset_index(name="count")
-            .sort_values("count", ascending=False).head(20)
-        )
-        if not venue_cnt.empty:
-            st.plotly_chart(
-                px.bar(venue_cnt, x="count", y="citing_venue", orientation="h",
-                       labels={"count": "Citations", "citing_venue": ""})
-                .update_layout(yaxis=dict(autorange="reversed"),
-                               xaxis_title="Citations", yaxis_title="", height=520),
-                use_container_width=True,
-            )
-
-    with col_v2:
-        st.subheader("CitationHub Field × Intent Distribution")
-        st.caption("How each field uses citations differently (all fields)")
-        fi_pct = (
-            seed[["seed_paper_id", "field"]]
-            .merge(events[["seed_paper_id", "primary_intent"]], on="seed_paper_id", how="inner")
-            .groupby(["field", "primary_intent"]).size().reset_index(name="count")
-        )
-        if not fi_pct.empty:
-            totals = fi_pct.groupby("field")["count"].transform("sum")
-            fi_pct["pct"] = (fi_pct["count"] / totals * 100).round(1)
-            n_fields = fi_pct["field"].nunique()
-            chart_height = max(520, n_fields * 28)
-            st.plotly_chart(
-                px.bar(fi_pct, x="pct", y="field", color="primary_intent",
-                       orientation="h", color_discrete_map=INTENT_COLORS,
-                       labels={"pct": "% of citations", "field": "", "primary_intent": "Intent"})
-                .update_layout(
-                    barmode="stack",
-                    yaxis=dict(autorange="reversed", categoryorder="total ascending"),
-                    xaxis_title="% of citations", yaxis_title="",
-                    legend_title="Intent", height=chart_height,
-                ),
-                use_container_width=True,
-            )
-
-    # ── Citation Trend over Time ────────────────────────────────
-    st.markdown("---")
-    st.subheader("Citation Trend over Time (selected paper)")
-    st.caption("How citations to the selected seed paper have changed year by year")
-    trend_sel = (seed_events.dropna(subset=["citing_year"])
-                 .assign(citing_year=lambda df: df["citing_year"].astype(int))
-                 .query("citing_year >= 2000")
-                 .groupby("citing_year").size().reset_index(name="count"))
-    if not trend_sel.empty:
-        st.plotly_chart(
-            px.line(trend_sel, x="citing_year", y="count", markers=True,
-                    labels={"citing_year": "Year", "count": "Citations"})
-            .update_layout(xaxis_title="Year", yaxis_title="Citations",
-                           hovermode="x unified"),
-            use_container_width=True)
-    else:
-        st.info("No citation trend data for the selected paper.")
-
-    # ── Export ─────────────────────────────────────────────────
-    st.markdown("---")
-    st.subheader("Export Data")
-    col_e1, col_e2, col_e3 = st.columns(3)
-
-    with col_e1:
-        csv_seed = seed_filtered[
-            ["title", "doi", "journal", "author", "country", "field", "citedby_count"]
-        ].to_csv(index=False).encode("utf-8")
-        st.download_button(
-            "⬇ Seed Papers (CSV)",
-            csv_seed, "seed_papers.csv", "text/csv",
-            use_container_width=True,
-        )
-
-    with col_e2:
-        cite_export = seed_events[
-            ["citing_title", "citing_doi", "citing_year", "citing_venue",
-             "primary_intent", "context_count", "is_influential"]
-        ].rename(columns={
-            "citing_title": "title", "citing_doi": "doi",
-            "citing_year": "year", "citing_venue": "venue",
-            "primary_intent": "intent", "context_count": "contexts",
-            "is_influential": "influential",
-        }).to_csv(index=False).encode("utf-8")
-        st.download_button(
-            "⬇ Citation Events (CSV)",
-            cite_export, "citation_events.csv", "text/csv",
-            use_container_width=True,
-        )
-
-    with col_e3:
-        intent_csv = intent_summary.to_csv(index=False).encode("utf-8")
-        st.download_button(
-            "⬇ Intent Summary (CSV)",
-            intent_csv, "intent_summary.csv", "text/csv",
-            use_container_width=True,
-        )
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from typing import List
+
+import pandas as pd
+import networkx as nx
+import streamlit as st
+import plotly.express as px
+import plotly.graph_objects as go
+from pyvis.network import Network
+import streamlit.components.v1 as components
+
+HF_REPO_ID = os.environ.get("HF_REPO_ID", "")
+HF_TOKEN   = os.environ.get("HF_TOKEN", "")
+
+st.set_page_config(page_title="CitationHub", page_icon="📚", layout="wide")
+
+ALLOWED_INTENTS = [
+    "background","uses","similarities","motivation",
+    "differences","future_work","extends",
+]
+INTENT_COLORS = {
+    "background":"#94a3b8","uses":"#22c55e","similarities":"#3b82f6",
+    "motivation":"#f59e0b","differences":"#ef4444",
+    "future_work":"#8b5cf6","extends":"#06b6d4",
+}
+NODE_COLORS = {
+    "seed_paper":"#111827","citing_paper":"#dbeafe","citation_event":"#fde68a",
+    "journal":"#ede9fe","author":"#fee2e2","affiliation":"#fae8ff",
+    "city":"#cffafe","country":"#ffedd5","field":"#e0e7ff","intent":"#dcfce7",
+}
+NODE_TYPE_COLORS = {
+    "seed_paper":"#111827","citing_paper":"#3b82f6","citation_event":"#f59e0b",
+    "journal":"#8b5cf6","author":"#ef4444","affiliation":"#ec4899",
+    "city":"#06b6d4","country":"#f97316","field":"#6366f1","intent":"#22c55e",
+}
+
+DEFAULT_DATA_DIR = Path(os.environ.get(
+    "CITATIONHUB_DATA_DIR",
+    r"C:\Users\user\OneDrive\바탕 화면\Citehub_huggingface\data",
+))
+
+
+def fmt_num(x):
+    try: return f"{int(x):,}"
+    except: return "-"
+
+
+def _hf_download(filename: str) -> str:
+    from huggingface_hub import hf_hub_download
+    return hf_hub_download(
+        repo_id=HF_REPO_ID, repo_type="dataset",
+        filename=f"data/{filename}", token=HF_TOKEN or None,
+    )
+
+
+def _read(filename: str, data_dir: Path | None = None) -> pd.DataFrame:
+    if HF_REPO_ID:
+        return pd.read_parquet(_hf_download(filename))
+    return pd.read_parquet(data_dir / filename)
+
+
+def plotly_network_fig(
+    nodes_df: pd.DataFrame,
+    edges_df: pd.DataFrame,
+    title: str = "",
+    height: int = 750,
+    seed_node_ids: list | None = None,
+) -> go.Figure:
+    """SVG 기반 Plotly 네트워크 그래프 — 확대해도 선명."""
+    G = nx.Graph()
+    node_meta: dict = {}
+    for _, row in nodes_df.iterrows():
+        nid = str(row["node_id"])
+        G.add_node(nid)
+        node_meta[nid] = row
+
+    for _, row in edges_df.iterrows():
+        s, t = str(row["source"]), str(row["target"])
+        if s in node_meta and t in node_meta:
+            G.add_edge(s, t, edge_type=row.get("edge_type", ""))
+
+    if len(G.nodes) == 0:
+        return go.Figure()
+
+    k = max(1.5, 3.0 / (len(G.nodes) ** 0.4))
+    pos = nx.spring_layout(G, seed=42, k=k, iterations=60)
+
+    # ── edges ─────────────────────────────────
+    ex, ey = [], []
+    for src, tgt in G.edges():
+        x0, y0 = pos.get(src, (0, 0))
+        x1, y1 = pos.get(tgt, (0, 0))
+        ex += [x0, x1, None]
+        ey += [y0, y1, None]
+
+    traces: list[go.BaseTraceType] = [
+        go.Scatter(
+            x=ex, y=ey, mode="lines",
+            line=dict(width=0.8, color="#cbd5e1"),
+            hoverinfo="none", showlegend=False,
+        )
+    ]
+
+    # ── nodes grouped by type ─────────────────
+    for ntype, color in NODE_TYPE_COLORS.items():
+        subset = nodes_df[nodes_df["node_type"] == ntype]
+        if subset.empty:
+            continue
+        xs, ys, hovers, texts = [], [], [], []
+        for _, row in subset.iterrows():
+            nid = str(row["node_id"])
+            if nid not in pos:
+                continue
+            x, y = pos[nid]
+            xs.append(x); ys.append(y)
+            label = str(row.get("label", ""))[:50]
+            texts.append(label if ntype == "seed_paper" else "")
+            hovers.append(
+                f"<b>{label}</b><br>"
+                f"Type: {ntype}<br>"
+                f"DOI: {row.get('doi','') or '-'}<br>"
+                f"Pub: {row.get('publication_name','') or '-'}<br>"
+                f"Group: {row.get('group','') or '-'}"
+            )
+
+        is_seed = ntype == "seed_paper"
+        traces.append(go.Scatter(
+            x=xs, y=ys,
+            mode="markers+text" if is_seed else "markers",
+            text=texts, textposition="top center",
+            hovertext=hovers, hoverinfo="text",
+            name=ntype,
+            marker=dict(
+                size=20 if is_seed else 10,
+                color=color,
+                line=dict(width=1.5 if is_seed else 0.5, color="white"),
+                symbol="circle",
+            ),
+        ))
+
+    fig = go.Figure(data=traces)
+    fig.update_layout(
+        title=dict(text=title, font=dict(size=14)),
+        showlegend=True,
+        legend=dict(title="Node type", itemsizing="constant"),
+        hovermode="closest",
+        height=height,
+        margin=dict(l=0, r=0, t=40 if title else 10, b=0),
+        paper_bgcolor="white",
+        plot_bgcolor="#f8fafc",
+        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+    )
+    return fig
+
+
+def plotly_ontology_fig(height: int = 820) -> go.Figure:
+    """CitationHub 온톨로지 구조 — Plotly SVG. 각 노드에 속성값 표시."""
+
+    # 각 노드 타입의 주요 속성 (노드 아래에 작게 표시)
+    NODE_PROPS = {
+        "seed_paper":     "doi · title · journal\nauthor · affiliation\ncountry · field · citedby_count",
+        "citation_event": "event_id · citing_year\nprimary_intent · context\nis_influential",
+        "citing_paper":   "doi · title\nyear · venue · oa_pdf",
+        "intent":         "background · uses\nsimilarities · motivation\ndifferences · future_work · extends",
+        "journal":        "journal_name",
+        "author":         "author_name · author_id",
+        "affiliation":    "affiliation_name",
+        "city":           "city_name",
+        "country":        "country_name",
+        "field":          "field_name",
+    }
+
+    node_defs = [
+        ("seed",        "Top5PctCitedPaper", "seed_paper"),
+        ("event",       "CitationEvent",     "citation_event"),
+        ("citing",      "CitingPaper",        "citing_paper"),
+        ("intent",      "Intent",             "intent"),
+        ("journal",     "Journal",            "journal"),
+        ("author",      "Author",             "author"),
+        ("affiliation", "Affiliation",        "affiliation"),
+        ("city",        "City",               "city"),
+        ("country",     "Country",            "country"),
+        ("field",       "Field",              "field"),
+    ]
+    edge_defs = [
+        ("event","citing","hasCitingPaper"),    ("event","seed","hasCitedPaper"),
+        ("event","intent","hasPrimaryIntent"),   ("seed","journal","publishedInJournal"),
+        ("seed","author","hasAuthor"),           ("seed","affiliation","hasAffiliation"),
+        ("seed","city","locatedInCity"),         ("seed","country","locatedInCountry"),
+        ("seed","field","belongsToField"),
+    ]
+    G = nx.DiGraph()
+    for nid, _, _ in node_defs:
+        G.add_node(nid)
+    for s, t, _ in edge_defs:
+        G.add_edge(s, t)
+
+    pos = nx.spring_layout(G, seed=7, k=2.5, iterations=80)
+
+    # ── 엣지 라인 + 엣지 레이블 ───────────────────────────────
+    ex, ey = [], []
+    ann = []
+    for s, t, lbl in edge_defs:
+        x0, y0 = pos[s]; x1, y1 = pos[t]
+        ex += [x0, x1, None]; ey += [y0, y1, None]
+        mx, my = (x0+x1)/2, (y0+y1)/2
+        ann.append(dict(
+            x=mx, y=my, text=f"<i>{lbl}</i>",
+            showarrow=False, font=dict(size=9, color="#64748b"),
+            bgcolor="rgba(255,255,255,0.75)",
+        ))
+
+    traces: list[go.BaseTraceType] = [
+        go.Scatter(x=ex, y=ey, mode="lines",
+                   line=dict(width=1.2, color="#94a3b8"),
+                   hoverinfo="none", showlegend=False)
+    ]
+
+    # ── 노드 + 속성 annotation ────────────────────────────────
+    for nid, label, ntype in node_defs:
+        x, y = pos[nid]
+        color = NODE_TYPE_COLORS.get(ntype, "#94a3b8")
+        props = NODE_PROPS.get(ntype, "")
+
+        # 노드 마커 + 상단 이름 레이블
+        traces.append(go.Scatter(
+            x=[x], y=[y], mode="markers+text",
+            text=[f"<b>{label}</b>"], textposition="top center",
+            hoverinfo="text",
+            hovertext=(f"<b>{label}</b><br>Type: {ntype}<br>"
+                       + props.replace("\n", "<br>")),
+            name=label, showlegend=False,
+            marker=dict(size=24, color=color,
+                        line=dict(width=1.5, color="white")),
+            textfont=dict(size=11, color="#1e293b"),
+        ))
+
+        # 속성값: 노드 아래에 작은 글씨 annotation
+        if props:
+            prop_html = props.replace("\n", "<br>")
+            ann.append(dict(
+                x=x, y=y,
+                text=f"<span style='font-size:8px;color:#64748b'>{prop_html}</span>",
+                showarrow=False,
+                xanchor="center",
+                yanchor="top",
+                yshift=-22,          # 노드 마커 아래로 오프셋
+                font=dict(size=8, color="#64748b"),
+                bgcolor="rgba(248,250,252,0.85)",
+                borderpad=2,
+            ))
+
+    fig = go.Figure(data=traces)
+    fig.update_layout(
+        showlegend=False, hovermode="closest", height=height,
+        annotations=ann,
+        margin=dict(l=10, r=10, t=20, b=10),
+        paper_bgcolor="white", plot_bgcolor="#f8fafc",
+        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+    )
+    return fig
+
+
+def inject_fullscreen(html: str) -> str:
+    extra = """
+    <button onclick="var el=document.getElementById('mynetwork');
+      if(el){if(el.requestFullscreen)el.requestFullscreen();
+      else if(el.webkitRequestFullscreen)el.webkitRequestFullscreen();}"
+      style="position:fixed;bottom:18px;right:18px;z-index:9999;
+             padding:8px 18px;background:#1e293b;color:white;border:none;
+             border-radius:8px;cursor:pointer;font-size:13px;
+             box-shadow:0 2px 8px rgba(0,0,0,0.35);">⛶ Fullscreen</button>
+    <div style="position:fixed;bottom:18px;left:18px;z-index:9999;font-size:12px;
+                color:#64748b;background:rgba(255,255,255,0.85);
+                padding:5px 10px;border-radius:6px;">
+      🖱 Scroll: zoom &nbsp;|&nbsp; Drag: pan &nbsp;|&nbsp; Click node: info</div>
+    <script>
+    // HiDPI 캔버스 해상도 보정 (Canvas 흐림 최소화)
+    (function fixDPI() {
+      var canvas = document.querySelector('#mynetwork canvas');
+      if (!canvas) { setTimeout(fixDPI, 200); return; }
+      var dpr = window.devicePixelRatio || 1;
+      if (dpr <= 1) return;
+      try {
+        if (typeof network !== 'undefined') {
+          network.canvas.pixelRatio = dpr;
+          network.redraw();
+        }
+      } catch(e) {}
+    })();
+    </script>
+    """
+    return html.replace("</body>", extra + "</body>")
+
+
+# ── 메인 데이터 로드 (11개) ────────────────────────────────────
+@st.cache_data(show_spinner=False)
+def load_data(data_dir_str: str):
+    d = None if HF_REPO_ID else Path(data_dir_str)
+
+    seed_df    = _read("seed_cited_papers_normalized.parquet", d)
+    events_df  = _read("citation_events_normalized.parquet", d)
+    citing_df  = _read("citing_papers_normalized.parquet", d)
+    authors_df      = _read("authors.parquet", d)
+    affiliations_df = _read("affiliations.parquet", d)
+    aff_geo_df      = _read("affiliation_geo.parquet", d)
+    cities_df       = _read("cities.parquet", d)
+    countries_df    = _read("countries.parquet", d)
+    fields_df       = _read("fields.parquet", d)
+    intents_df      = _read("intents.parquet", d)
+    journals_df     = _read("journals.parquet", d)
+
+    seed = pd.DataFrame({
+        "seed_paper_id":  seed_df["seed_paper_id"],
+        "doi":            seed_df.get("doi", pd.Series(dtype=str)).fillna(""),
+        "title":          seed_df.get("title", pd.Series(dtype=str)).fillna(""),
+        "journal":        seed_df.get("publication_name", pd.Series(dtype=str)).fillna(""),
+        "author":         seed_df.get("creator", pd.Series(dtype=str)).fillna(""),
+        "affiliation":    seed_df.get("affilname", pd.Series(dtype=str)).fillna(""),
+        "city":           seed_df.get("affiliation_city", pd.Series(dtype=str)).fillna(""),
+        "country":        seed_df.get("affiliation_country", pd.Series(dtype=str)).fillna(""),
+        "field":          seed_df.get("group", pd.Series(dtype=str)).fillna(""),
+        "cover_date":     seed_df.get("cover_date", pd.Series(dtype=str)).fillna(""),
+        "citedby_count":  pd.to_numeric(seed_df.get("citedby_count"), errors="coerce").fillna(0).astype(int),
+        "author_id":      seed_df.get("author_id", pd.Series(dtype=object)),
+        "affiliation_id": seed_df.get("affiliation_id", pd.Series(dtype=object)),
+        "country_id":     seed_df.get("country_id", pd.Series(dtype=object)),
+        "field_id":       seed_df.get("field_id", pd.Series(dtype=object)),
+        "journal_id":     seed_df.get("journal_id", pd.Series(dtype=object)),
+    })
+    for col in ["title","doi","journal","field","country"]:
+        seed[f"{col}_lc"] = seed[col].astype(str).str.lower()
+    seed = seed.sort_values(["citedby_count","title"], ascending=[False,True]).reset_index(drop=True)
+
+    events = pd.DataFrame({
+        "citation_event_id": events_df["citation_event_id"],
+        "seed_paper_id":     events_df["cited_seed_paper_id"],
+        "citing_paper_id":   events_df["citing_paper_id"],
+        "citing_title":      events_df.get("citing_title", pd.Series(dtype=str)).fillna(""),
+        "citing_doi":        events_df.get("citing_doi", pd.Series(dtype=str)).fillna(""),
+        "citing_year":       pd.to_numeric(events_df.get("citing_year"), errors="coerce"),
+        "citing_venue":      events_df.get("citing_venue", pd.Series(dtype=str)).fillna(""),
+        "primary_intent":    events_df.get("primary_intent", pd.Series(dtype=str)).fillna(""),
+        "contexts":          events_df.get("contexts"),
+        "context_count":     pd.to_numeric(events_df.get("context_count"), errors="coerce").fillna(0).astype(int),
+        "intent_count":      pd.to_numeric(events_df.get("intent_count"), errors="coerce").fillna(0).astype(int),
+        "is_influential":    events_df.get("is_influential", pd.Series(dtype=bool)).fillna(False),
+        "field_id":          events_df.get("field_id", pd.Series(dtype=object)),
+    })
+    events = events[events["primary_intent"].isin(ALLOWED_INTENTS)].reset_index(drop=True)
+
+    citing = pd.DataFrame({
+        "citing_paper_id": citing_df["citing_paper_id"],
+        "doi":    citing_df.get("doi",   pd.Series(dtype=str)).fillna(""),
+        "title":  citing_df.get("title", pd.Series(dtype=str)).fillna(""),
+        "year":   pd.to_numeric(citing_df.get("year"), errors="coerce"),
+        "venue":  citing_df.get("venue", pd.Series(dtype=str)).fillna(""),
+        "oa_pdf": citing_df.get("oa_pdf",pd.Series(dtype=str)).fillna(""),
+    })
+
+    filters = {
+        "fields":    sorted([x for x in seed["field"].dropna().astype(str).unique() if x]),
+        "countries": sorted([x for x in seed["country"].dropna().astype(str).unique() if x]),
+        "journals":  sorted([x for x in seed["journal"].dropna().astype(str).unique() if x]),
+        "intents":   ALLOWED_INTENTS,
+        "year_min":  int(events["citing_year"].dropna().min()) if events["citing_year"].notna().any() else 2000,
+        "year_max":  int(events["citing_year"].dropna().max()) if events["citing_year"].notna().any() else 2025,
+    }
+    overview = {
+        "seed_papers":     int(len(seed)),
+        "citation_events": int(len(events)),
+        "citing_papers":   int(events["citing_paper_id"].nunique()),
+        "authors":         int(len(authors_df)),
+        "journals":        int(seed["journal"].replace("", pd.NA).dropna().nunique()),
+        "countries":       int(seed["country"].replace("", pd.NA).dropna().nunique()),
+        "fields":          int(seed["field"].replace("", pd.NA).dropna().nunique()),
+        "intents":         len(ALLOWED_INTENTS),
+    }
+    return (seed, events, citing, filters, overview,
+            authors_df, affiliations_df, aff_geo_df,
+            cities_df, countries_df, fields_df, intents_df, journals_df)
+
+
+# ── KG 데이터: DuckDB 방식으로 분리 로드 ─────────────────────
+#   kg_nodes  : pandas 전체 로드 (~160MB 파일, 메모리 허용 범위)
+#   kg_edges  : DuckDB로 필요한 노드의 엣지만 쿼리 (전체 로드 안 함)
+#   enriched  : DuckDB로 집계 통계만 쿼리 (전체 로드 안 함)
+
+@st.cache_data(show_spinner=False)
+def load_kg_nodes(data_dir_str: str) -> pd.DataFrame:
+    """kg_nodes 전체 로드 (3.4M rows, ~160MB 파일)"""
+    d = None if HF_REPO_ID else Path(data_dir_str)
+    return _read("kg_nodes.parquet", d)
+
+
+@st.cache_data(show_spinner=False)
+def get_parquet_path(filename: str, data_dir_str: str) -> str:
+    """파일 경로 반환 (HF면 로컬 캐시에 다운로드 후 경로 반환)"""
+    if HF_REPO_ID:
+        return _hf_download(filename)
+    # DuckDB용: 역슬래시 → 슬래시 변환
+    return str(Path(data_dir_str) / filename).replace("\\", "/")
+
+
+@st.cache_data(show_spinner=False)
+def query_kg_edges_for_node(node_id: str, kg_edges_path: str, max_edges: int = 80) -> pd.DataFrame:
+    """DuckDB: 특정 노드의 엣지만 parquet에서 바로 쿼리 (전체 로드 없음)"""
+    import duckdb
+    safe_path = kg_edges_path.replace("\\", "/")
+    safe_node = node_id.replace("'", "''")
+    q = f"""
+    SELECT source, target, edge_type
+    FROM read_parquet('{safe_path}')
+    WHERE source = '{safe_node}' OR target = '{safe_node}'
+    LIMIT {int(max_edges)}
+    """
+    return duckdb.execute(q).df()
+
+
+@st.cache_data(show_spinner=False)
+def query_enriched_stats(enriched_path: str):
+    """DuckDB: enriched 전체 로드 없이 집계 통계만 쿼리"""
+    import duckdb
+    safe_path = enriched_path.replace("\\", "/")
+
+    sem_df = duckdb.execute(f"""
+        SELECT has_semantic_evidence, COUNT(*) AS count
+        FROM read_parquet('{safe_path}')
+        GROUP BY has_semantic_evidence
+    """).df()
+
+    field_df = duckdb.execute(f"""
+        SELECT field_folder AS field,
+               AVG(CAST(has_semantic_evidence AS INTEGER)) AS sem_ratio,
+               COUNT(*) AS event_count
+        FROM read_parquet('{safe_path}')
+        GROUP BY field_folder
+        ORDER BY sem_ratio DESC
+        LIMIT 20
+    """).df()
+
+    return sem_df, field_df
+
+
+@st.cache_data(show_spinner=False)
+def query_explorer_edges(node_id: str, kg_edges_path: str, max_edges: int = 60) -> pd.DataFrame:
+    """DuckDB: KG Explorer용 임의 노드 엣지 쿼리"""
+    import duckdb
+    safe_path = kg_edges_path.replace("\\", "/")
+    safe_node = node_id.replace("'", "''")
+    q = f"""
+    SELECT source, target, edge_type
+    FROM read_parquet('{safe_path}')
+    WHERE source = '{safe_node}' OR target = '{safe_node}'
+    LIMIT {int(max_edges)}
+    """
+    return duckdb.execute(q).df()
+
+
+# ── 헬퍼 ───────────────────────────────────────────────────────
+def filter_seed_papers(seed, q, fields, countries, journals):
+    df = seed.copy()
+    q = (q or "").strip().lower()
+    if q:
+        df = df[df["title_lc"].str.contains(q, na=False) | df["doi_lc"].str.contains(q, na=False)]
+    if fields:    df = df[df["field"].str.lower().isin({x.lower() for x in fields})]
+    if countries: df = df[df["country"].str.lower().isin({x.lower() for x in countries})]
+    if journals:  df = df[df["journal"].str.lower().isin({x.lower() for x in journals})]
+    return df.reset_index(drop=True)
+
+
+def event_subset(events, seed_paper_id, year_min, year_max):
+    df = events[events["seed_paper_id"] == seed_paper_id].copy()
+    df = df[df["citing_year"].fillna(-99999) >= year_min]
+    df = df[df["citing_year"].fillna(99999) <= year_max]
+    return df.reset_index(drop=True)
+
+
+def build_intent_summary(df):
+    counts = df.groupby("primary_intent").size().to_dict()
+    return pd.DataFrame({"intent": ALLOWED_INTENTS,
+                          "count": [int(counts.get(i,0)) for i in ALLOWED_INTENTS]})
+
+
+def build_context_rows(df, limit=20):
+    rows = []
+    df = df.sort_values(["context_count","intent_count","citing_year"],
+                        ascending=[False,False,False], na_position="last")
+    for _, row in df.iterrows():
+        ctx = row["contexts"]
+        if isinstance(ctx, list) and ctx:
+            for c in ctx[:2]:
+                rows.append({"primary_intent": row["primary_intent"],
+                             "citing_title": row["citing_title"],
+                             "citing_doi": row["citing_doi"],
+                             "citing_year": None if pd.isna(row["citing_year"]) else int(row["citing_year"]),
+                             "context": c})
+        if len(rows) >= limit: break
+    return pd.DataFrame(rows[:limit])
+
+
+def build_citing_table(df, limit=30):
+    if df.empty:
+        return pd.DataFrame(columns=["citing_title","citing_year","primary_intent","context_count"])
+    return (df.sort_values(["context_count","intent_count","citing_year"],
+                            ascending=[False,False,False], na_position="last")
+            [["citing_paper_id","citing_title","citing_doi","citing_year","primary_intent","context_count"]]
+            .drop_duplicates(subset=["citing_paper_id"]).head(limit))
+
+
+def get_cocited_papers(selected_seed_id, events, seed, top_n=15):
+    """선택된 seed paper를 인용한 논문들이 함께 인용한 다른 seed papers"""
+    citing_ids = events[events["seed_paper_id"] == selected_seed_id]["citing_paper_id"].unique()
+    cocited = (events[events["citing_paper_id"].isin(citing_ids) &
+                      (events["seed_paper_id"] != selected_seed_id)]
+               .groupby("seed_paper_id").size()
+               .reset_index(name="co_citation_count")
+               .sort_values("co_citation_count", ascending=False)
+               .head(top_n))
+    return cocited.merge(seed[["seed_paper_id","title","field","journal","citedby_count"]],
+                         on="seed_paper_id", how="left")
+
+
+def get_kg_subgraph(seed_doi: str, kg_nodes, kg_edges, max_edges=80):
+    """선택된 seed paper의 KG 1-hop 서브그래프 반환"""
+    node_id = f"seed:{seed_doi}"
+    edges = kg_edges[(kg_edges["source"] == node_id) |
+                     (kg_edges["target"] == node_id)].head(max_edges)
+    if edges.empty:
+        return None, None
+    all_node_ids = set(edges["source"].tolist()) | set(edges["target"].tolist())
+    nodes = kg_nodes[kg_nodes["node_id"].isin(all_node_ids)]
+    return nodes, edges
+
+
+def get_explorer_subgraph(search_node_id: str, kg_nodes, kg_edges, max_edges=60):
+    """KG Explorer: 임의 노드 기준 서브그래프"""
+    edges = kg_edges[(kg_edges["source"] == search_node_id) |
+                     (kg_edges["target"] == search_node_id)].head(max_edges)
+    if edges.empty:
+        return None, None
+    all_ids = set(edges["source"].tolist()) | set(edges["target"].tolist())
+    nodes = kg_nodes[kg_nodes["node_id"].isin(all_ids)]
+    return nodes, edges
+
+
+# ── pyvis 빌더 ─────────────────────────────────────────────────
+def pyvis_citation_graph(seed_row, events_df):
+    net = Network(height="780px", width="100%", bgcolor="#ffffff", font_color="#111827", directed=True)
+    sid = seed_row["seed_paper_id"]
+    net.add_node(sid, label=seed_row["title"][:60], color="#111827", size=34, shape="dot",
+                 font={"color":"white"})
+    for _, row in events_df.sort_values(["context_count","intent_count"],
+                                         ascending=False).head(40).iterrows():
+        cid = row["citing_paper_id"]
+        net.add_node(cid, label=(row["citing_title"] or row["citing_doi"] or cid)[:60],
+                     color=NODE_COLORS["citing_paper"], size=18, shape="dot")
+        ctx = (row["contexts"] or [])[0] if isinstance(row["contexts"], list) and row["contexts"] else ""
+        yr  = "" if pd.isna(row["citing_year"]) else int(row["citing_year"])
+        net.add_edge(cid, sid, label=row["primary_intent"],
+                     color=INTENT_COLORS.get(row["primary_intent"],"#94a3b8"),
+                     title=f"Intent: {row['primary_intent']}<br>Year: {yr}<br>{ctx}")
+    net.barnes_hut()
+    return inject_fullscreen(net.generate_html())
+
+
+def pyvis_ontology():
+    net = Network(height="780px", width="100%", bgcolor="#ffffff", font_color="#111827", directed=True)
+    for nid, label, typ in [
+        ("seed","Top5PctCitedPaper","seed_paper"),("event","CitationEvent","citation_event"),
+        ("citing","CitingPaper","citing_paper"),  ("intent","Intent","intent"),
+        ("journal","Journal","journal"),           ("author","Author","author"),
+        ("affiliation","Affiliation","affiliation"),("city","City","city"),
+        ("country","Country","country"),           ("field","Field","field"),
+    ]:
+        net.add_node(nid, label=label, color=NODE_COLORS[typ], size=24)
+    for s, t, l in [
+        ("event","citing","hasCitingPaper"),("event","seed","hasCitedPaper"),
+        ("event","intent","hasPrimaryIntent"),("seed","journal","publishedInJournal"),
+        ("seed","author","hasAuthor"),        ("seed","affiliation","hasAffiliation"),
+        ("seed","city","locatedInCity"),      ("seed","country","locatedInCountry"),
+        ("seed","field","belongsToField"),
+    ]:
+        net.add_edge(s, t, label=l)
+    net.barnes_hut()
+    return inject_fullscreen(net.generate_html())
+
+
+def pyvis_from_kg(nodes_df, edges_df, height="780px"):
+    """kg_nodes / kg_edges DataFrame으로 pyvis 그래프 생성"""
+    net = Network(height=height, width="100%", bgcolor="#ffffff", font_color="#111827", directed=True)
+    for _, row in nodes_df.iterrows():
+        ntype = row.get("node_type","")
+        color = NODE_TYPE_COLORS.get(ntype,"#94a3b8")
+        label = str(row.get("label",""))[:55]
+        size  = 30 if ntype == "seed_paper" else 16
+        font  = {"color":"white"} if ntype == "seed_paper" else {}
+        tooltip = f"Type: {ntype}<br>DOI: {row.get('doi','')}<br>Pub: {row.get('publication_name','')}"
+        net.add_node(str(row["node_id"]), label=label, color=color,
+                     size=size, shape="dot", title=tooltip, font=font)
+    for _, row in edges_df.iterrows():
+        net.add_edge(str(row["source"]), str(row["target"]),
+                     label=row.get("edge_type",""), color="#94a3b8")
+    net.barnes_hut()
+    return inject_fullscreen(net.generate_html())
+
+
+# ═══════════════════════════════════════════════════════════════
+#  메인 UI
+# ═══════════════════════════════════════════════════════════════
+st.title("CitationHub")
+st.caption("Explore influential papers (top 5% cited), their citation networks, and knowledge graphs.")
+
+# ── Sidebar ────────────────────────────────────────────────────
+with st.sidebar:
+    st.subheader("Data source")
+    if HF_REPO_ID:
+        data_dir_val = "hf"
+        st.caption(f"Hugging Face: {HF_REPO_ID}")
+    else:
+        data_dir_val = st.text_input("Parquet directory", str(DEFAULT_DATA_DIR))
+
+    try:
+        (seed, events, citing, filters, overview,
+         authors_df, affiliations_df, aff_geo_df,
+         cities_df, countries_df, fields_df, intents_df, journals_df) = load_data(data_dir_val)
+        st.success("Data loaded")
+    except Exception as e:
+        st.error(str(e)); st.stop()
+
+    st.subheader("Search seed papers")
+    q_input = st.text_input("Title or DOI")
+    if "q_submit" not in st.session_state: st.session_state["q_submit"] = ""
+    if st.button("Search", use_container_width=True):
+        st.session_state["q_submit"] = q_input
+
+    fields_sel    = st.multiselect("Field", filters["fields"])
+    countries_sel = st.multiselect("Country", filters["countries"])
+    journals_sel  = st.multiselect("Journal", filters["journals"][:200])
+    y_min = max(2000, filters["year_min"])
+    year_min, year_max = st.slider("Citing year", y_min, filters["year_max"], (y_min, filters["year_max"]))
+
+    seed_filtered = filter_seed_papers(seed, st.session_state["q_submit"],
+                                       fields_sel, countries_sel, journals_sel)
+
+    st.subheader("Overview counts")
+    c1, c2 = st.columns(2)
+    c1.metric("Seed papers",     fmt_num(overview["seed_papers"]))
+    c2.metric("Citation events", fmt_num(overview["citation_events"]))
+    c1.metric("Citing papers",   fmt_num(overview["citing_papers"]))
+    c2.metric("Authors",         fmt_num(overview["authors"]))
+    c1.metric("Countries",       fmt_num(overview["countries"]))
+    c2.metric("Fields",          fmt_num(overview["fields"]))
+
+    options = seed_filtered["seed_paper_id"].tolist()
+    if not options:
+        st.warning("No seed papers match the current search."); st.stop()
+    current     = st.session_state.get("selected_seed_id", options[0])
+    default_idx = options.index(current) if current in options else 0
+    selected_seed_id = st.selectbox(
+        "Seed paper", options, index=default_idx,
+        format_func=lambda sid: seed_filtered.loc[
+            seed_filtered["seed_paper_id"]==sid, "title"].iloc[0],
+    )
+    st.session_state["selected_seed_id"] = selected_seed_id
+
+selected_seed  = seed_filtered[seed_filtered["seed_paper_id"]==selected_seed_id].iloc[0]
+seed_events    = event_subset(events, selected_seed_id, year_min, year_max)
+intent_summary = build_intent_summary(seed_events)
+contexts_df    = build_context_rows(seed_events)
+citing_table   = build_citing_table(seed_events)
+
+# ── 탭 ─────────────────────────────────────────────────────────
+(tab_overview, tab_cnet,
+ tab_kg_exp, tab_geo, tab_analytics) = st.tabs([
+    "Overview","Citation Network",
+    "Knowledge Graph","Geographic Map","Analytics",
+])
+
+
+# ═══ 1. OVERVIEW ═══════════════════════════════════════════════
+with tab_overview:
+    col1, col2 = st.columns(2)
+    with col1:
+        st.subheader("Seed paper detail")
+        dc1, dc2 = st.columns(2)
+        dc1.metric("Cited by",        fmt_num(selected_seed["citedby_count"]))
+        dc2.metric("Citation events", fmt_num(len(seed_events)))
+        for label, key in [
+            ("Title","title"),("DOI","doi"),("Published","cover_date"),
+            ("Journal","journal"),("Author","author"),("Affiliation","affiliation"),
+            ("City","city"),("Country","country"),("Field","field"),
+        ]:
+            st.markdown(f"**{label}**  \n{selected_seed[key] or '-'}")
+
+        st.subheader("Related citing papers")
+        st.dataframe(citing_table.rename(columns={
+            "citing_title":"Title","citing_year":"Year",
+            "primary_intent":"Intent","context_count":"Contexts"}),
+            use_container_width=True, hide_index=True)
+
+        st.subheader("Co-cited seed papers")
+        st.caption("같은 citing paper에 의해 함께 인용된 다른 top 5% 논문들")
+        cocited = get_cocited_papers(selected_seed_id, events, seed)
+        if cocited.empty:
+            st.info("Co-cited papers not found.")
+        else:
+            st.dataframe(cocited.rename(columns={
+                "co_citation_count":"Co-citations","title":"Title",
+                "field":"Field","citedby_count":"Cited by"}),
+                use_container_width=True, hide_index=True)
+
+    with col2:
+        st.subheader("Intent distribution (selected paper)")
+        fig = px.bar(intent_summary, x="intent", y="count", color="intent",
+                     color_discrete_map=INTENT_COLORS)
+        fig.update_layout(showlegend=False, xaxis_title="", yaxis_title="Count")
+        st.plotly_chart(fig, use_container_width=True)
+
+        st.subheader("CitationHub Intent Distribution")
+        all_intents = events.groupby("primary_intent").size().to_dict()
+        ai_df = pd.DataFrame({"intent": ALLOWED_INTENTS,
+                               "count": [int(all_intents.get(i, 0)) for i in ALLOWED_INTENTS]})
+        fig2 = px.bar(ai_df, x="intent", y="count", color="intent",
+                      color_discrete_map=INTENT_COLORS)
+        fig2.update_layout(showlegend=False, xaxis_title="", yaxis_title="Count")
+        st.plotly_chart(fig2, use_container_width=True)
+
+        st.subheader("CitationHub Field Distribution")
+        fd = (seed_filtered.groupby("field", dropna=False).size()
+              .reset_index(name="count").sort_values("count", ascending=False).head(20))
+        fd["field"] = fd["field"].replace("","Unknown")
+        st.plotly_chart(
+            px.bar(fd, x="field", y="count").update_layout(xaxis_title="", yaxis_title="Count"),
+            use_container_width=True)
+
+    st.subheader("Citation contexts")
+    if contexts_df.empty:
+        st.info("No contexts available.")
+    else:
+        for _, row in contexts_df.iterrows():
+            st.markdown(
+                f"""<div style="border:1px solid #e2e8f0;border-radius:14px;padding:12px;
+                margin-bottom:10px;background:#f8fafc;">
+                <div style="display:inline-block;background:{INTENT_COLORS.get(row['primary_intent'],'#64748b')};
+                color:white;border-radius:999px;padding:4px 8px;font-size:12px;margin-bottom:6px;">
+                {row['primary_intent']}</div>
+                <div style="font-size:12px;color:#64748b;margin-bottom:6px;">
+                {row['citing_year'] or '-'} · {row['citing_title'] or row['citing_doi']}</div>
+                <div>{row['context']}</div></div>""",
+                unsafe_allow_html=True)
+
+
+# ═══ 2. CITATION NETWORK ════════════════════════════════════════
+with tab_cnet:
+    st.subheader("Citation Network")
+    st.caption("🖱 Scroll: zoom  |  Drag: pan  |  Click node: info  |  ⛶ button: fullscreen")
+    if seed_events.empty:
+        st.info("No citation network data for this seed paper.")
+    else:
+        components.html(pyvis_citation_graph(selected_seed, seed_events), height=820, scrolling=True)
+
+
+# ═══ 3. ONTOLOGY ════════════════════════════════════════════════
+# ═══ 3. KNOWLEDGE GRAPH (KG Explorer + Ontology) ════════════════
+with tab_kg_exp:
+    st.subheader("Knowledge Graph")
+
+    # ── CitationHub Ontology ─────────────────────────────────────
+    with st.expander("CitationHub Ontology — Node types & Properties", expanded=False):
+        st.plotly_chart(plotly_ontology_fig(height=820), use_container_width=True)
+
+    st.markdown("---")
+
+    try:
+        with st.spinner("Loading..."):
+            kg_nodes_exp  = load_kg_nodes(data_dir_val)
+            kg_edges_path = get_parquet_path("kg_edges.parquet", data_dir_val)
+
+        # ── 노드/엣지 타입 분포 통계
+        import duckdb as _ddb
+
+        nt = kg_nodes_exp["node_type"].value_counts().reset_index()
+        nt.columns = ["node_type", "count"]
+
+        et = _ddb.execute(f"""
+            SELECT edge_type, COUNT(*) AS count
+            FROM read_parquet('{kg_edges_path}')
+            GROUP BY edge_type ORDER BY count DESC
+        """).df()
+
+        col_a, col_b, col_c, col_d = st.columns([1, 2, 1, 2])
+        with col_a:
+            st.subheader("Node Types")
+            st.dataframe(nt, use_container_width=True, hide_index=True)
+        with col_b:
+            st.subheader("CitationHub KG Node Distribution")
+            nt_fig = px.bar(nt, x="node_type", y="count", color="node_type",
+                            color_discrete_map=NODE_TYPE_COLORS)
+            nt_fig.update_layout(showlegend=False, xaxis_title="", yaxis_title="Count")
+            st.plotly_chart(nt_fig, use_container_width=True)
+        with col_c:
+            st.subheader("Edge Types")
+            st.dataframe(et, use_container_width=True, hide_index=True)
+        with col_d:
+            st.subheader("CitationHub KG Edge Distribution")
+            et_fig = px.bar(et, x="edge_type", y="count", color="edge_type")
+            et_fig.update_layout(showlegend=False, xaxis_title="",
+                                 yaxis_title="Count", xaxis_tickangle=-35)
+            st.plotly_chart(et_fig, use_container_width=True)
+
+        # ── Multi-Node Knowledge Graph (2-hop: 10 node types + 10 edge types)
+        st.markdown("---")
+        st.subheader("Multi-Node Knowledge Graph")
+        st.caption("🖱 Scroll: zoom  |  Drag: pan  |  Click node: info  |  ⛶ button: fullscreen")
+
+        n_seeds = st.slider("Number of seed papers", 3, 15, 6, key="kg_exp_n_seeds")
+
+        # edge type당 고정 샘플 수 — 10 types × 10 = 최대 100 edges
+        EDGES_PER_TYPE = 10
+
+        with st.spinner("Querying graph..."):
+            top_seeds = (kg_nodes_exp[kg_nodes_exp["node_type"] == "seed_paper"]
+                         .sort_values("citedby_count", ascending=False)
+                         .head(n_seeds))
+            seed_ids = top_seeds["node_id"].tolist()
+
+            if seed_ids:
+                ids_sql = ", ".join(f"'{sid}'" for sid in seed_ids)
+
+                # 1-hop: seed paper에 연결된 모든 edge type
+                # → journal / author / affiliation / city / country / field / citation_event
+                hop1 = _ddb.execute(f"""
+                    WITH ranked AS (
+                        SELECT source, target, edge_type,
+                               ROW_NUMBER() OVER (
+                                   PARTITION BY edge_type ORDER BY source
+                               ) AS rn
+                        FROM read_parquet('{kg_edges_path}')
+                        WHERE source IN ({ids_sql}) OR target IN ({ids_sql})
+                    )
+                    SELECT source, target, edge_type FROM ranked
+                    WHERE rn <= {EDGES_PER_TYPE}
+                """).df()
+
+                # 2-hop: kg_nodes_exp 타입 기반으로 citation_event 노드 ID 추출
+                # (prefix 가정 없이 실제 node_type 컬럼으로 확인)
+                hop1_all_ids = set(hop1["source"].tolist()) | set(hop1["target"].tolist())
+                event_node_ids = (
+                    kg_nodes_exp[
+                        kg_nodes_exp["node_id"].isin(hop1_all_ids) &
+                        (kg_nodes_exp["node_type"] == "citation_event")
+                    ]["node_id"].tolist()[:40]
+                )
+
+                if event_node_ids:
+                    ev_sql = ", ".join(f"'{eid}'" for eid in event_node_ids)
+                    # citation_event → HAS_CITING_PAPER → citing_paper
+                    # citation_event → HAS_PRIMARY_INTENT → intent
+                    hop2 = _ddb.execute(f"""
+                        WITH ranked AS (
+                            SELECT source, target, edge_type,
+                                   ROW_NUMBER() OVER (
+                                       PARTITION BY edge_type ORDER BY source
+                                   ) AS rn
+                            FROM read_parquet('{kg_edges_path}')
+                            WHERE (source IN ({ev_sql}) OR target IN ({ev_sql}))
+                              AND edge_type NOT IN (
+                                  SELECT DISTINCT edge_type
+                                  FROM read_parquet('{kg_edges_path}')
+                                  WHERE source IN ({ids_sql}) OR target IN ({ids_sql})
+                              )
+                        )
+                        SELECT source, target, edge_type FROM ranked
+                        WHERE rn <= {EDGES_PER_TYPE}
+                    """).df()
+                    exp_edges = pd.concat([hop1, hop2]).drop_duplicates(
+                        subset=["source", "target", "edge_type"]
+                    )
+                else:
+                    exp_edges = hop1
+
+                all_exp_ids = set(exp_edges["source"].tolist()) | set(exp_edges["target"].tolist())
+                exp_nodes = kg_nodes_exp[kg_nodes_exp["node_id"].isin(all_exp_ids)]
+
+                c1, c2, c3, c4 = st.columns(4)
+                c1.metric("Nodes",      fmt_num(len(exp_nodes)))
+                c2.metric("Edges",      fmt_num(len(exp_edges)))
+                c3.metric("Node types", fmt_num(exp_nodes["node_type"].nunique()))
+                c4.metric("Edge types", fmt_num(exp_edges["edge_type"].nunique()))
+
+                kg_html = pyvis_from_kg(exp_nodes, exp_edges)
+                components.html(kg_html, height=860, scrolling=True)
+
+    except Exception as e:
+        st.error(str(e))
+
+
+# ═══ 6. GEOGRAPHIC MAP ══════════════════════════════════════════
+with tab_geo:
+    st.subheader("Geographic Distribution of Seed Papers")
+
+    country_cnt = (seed_filtered.groupby("country", dropna=False).size()
+                   .reset_index(name="count").rename(columns={"country":"country_name"}))
+    country_cnt = country_cnt[country_cnt["country_name"].str.strip() != ""]
+
+    if not country_cnt.empty:
+        fig_map = px.choropleth(country_cnt, locations="country_name",
+                                locationmode="country names", color="count",
+                                hover_name="country_name",
+                                color_continuous_scale="Blues",
+                                title="Seed Papers by Country")
+        fig_map.update_layout(geo=dict(showframe=False), height=500)
+        st.plotly_chart(fig_map, use_container_width=True)
+
+    st.subheader("Top Cities")
+    city_cnt = (seed_filtered.merge(
+                    aff_geo_df[["affiliation_name","city_name","country_name"]],
+                    left_on="affiliation", right_on="affiliation_name", how="left")
+                .groupby(["country_name","city_name"], dropna=False).size()
+                .reset_index(name="count").dropna(subset=["country_name"])
+                .sort_values("count", ascending=False).head(30))
+    if not city_cnt.empty:
+        st.plotly_chart(
+            px.bar(city_cnt, x="city_name", y="count", color="country_name",
+                   title="Top 30 Cities")
+            .update_layout(xaxis_title="", yaxis_title="# Seed Papers", xaxis_tickangle=-40),
+            use_container_width=True)
+
+    # ── Affiliation 시각화 ──────────────────────────────────────
+    st.subheader("Top Affiliations")
+    geo_col1, geo_col2 = st.columns(2)
+
+    with geo_col1:
+        aff_cnt = (seed_filtered[seed_filtered["affiliation"].str.strip() != ""]
+                   .groupby("affiliation").size()
+                   .reset_index(name="count")
+                   .sort_values("count", ascending=False).head(20))
+        if not aff_cnt.empty:
+            st.plotly_chart(
+                px.bar(aff_cnt, x="count", y="affiliation", orientation="h",
+                       title="Top 20 Affiliations by Seed Papers",
+                       labels={"count": "Seed Papers", "affiliation": ""})
+                .update_layout(yaxis=dict(autorange="reversed"),
+                               xaxis_title="Seed Papers", yaxis_title="", height=520),
+                use_container_width=True)
+
+    with geo_col2:
+        aff_country = (seed_filtered[
+                (seed_filtered["affiliation"].str.strip() != "") &
+                (seed_filtered["country"].str.strip() != "")
+            ]
+            .groupby(["country", "affiliation"]).size()
+            .reset_index(name="count")
+            .sort_values("count", ascending=False)
+        )
+        top_affs = aff_country.groupby("affiliation")["count"].sum().nlargest(20).index
+        aff_country_top = aff_country[aff_country["affiliation"].isin(top_affs)]
+        if not aff_country_top.empty:
+            st.plotly_chart(
+                px.bar(aff_country_top, x="count", y="affiliation",
+                       color="country", orientation="h",
+                       title="Top Affiliations by Country",
+                       labels={"count": "Seed Papers", "affiliation": "", "country": "Country"})
+                .update_layout(yaxis=dict(autorange="reversed"),
+                               barmode="stack",
+                               xaxis_title="Seed Papers", yaxis_title="",
+                               legend_title="Country", height=520),
+                use_container_width=True)
+
+
+# ═══ 7. ANALYTICS ═══════════════════════════════════════════════
+with tab_analytics:
+    col_a, col_b = st.columns(2)
+
+    with col_a:
+        st.subheader("Top Authors")
+        if "author_id" in seed.columns and not seed["author_id"].isna().all():
+            top_auth = (seed.explode("author_id")
+                        .merge(authors_df, on="author_id", how="left")
+                        .groupby("author_name").size()
+                        .reset_index(name="paper_count")
+                        .sort_values("paper_count", ascending=False).head(20))
+        else:
+            top_auth = (seed["author"].value_counts()
+                        .reset_index().rename(columns={"author":"author_name","count":"paper_count"})
+                        .head(20))
+        top_auth = top_auth[top_auth["author_name"].str.strip() != ""]
+        st.plotly_chart(
+            px.bar(top_auth, x="paper_count", y="author_name", orientation="h",
+                   title="Top 20 Authors")
+            .update_layout(yaxis=dict(autorange="reversed"),
+                           xaxis_title="Seed Papers", yaxis_title=""),
+            use_container_width=True)
+
+    with col_b:
+        st.subheader("Top Journals")
+        top_jnl = (seed.groupby("journal").size()
+                   .reset_index(name="count").sort_values("count", ascending=False).head(20))
+        top_jnl = top_jnl[top_jnl["journal"].str.strip() != ""]
+        st.plotly_chart(
+            px.bar(top_jnl, x="count", y="journal", orientation="h",
+                   title="Top 20 Journals")
+            .update_layout(yaxis=dict(autorange="reversed"),
+                           xaxis_title="Seed Papers", yaxis_title=""),
+            use_container_width=True)
+
+    st.markdown("---")
+    col_c, col_d = st.columns(2)
+
+    with col_c:
+        st.subheader("CitationHub Field × Intent Distribution Heatmap")
+        fi = (seed[["seed_paper_id","field"]]
+              .merge(events[["seed_paper_id","primary_intent"]], on="seed_paper_id", how="inner")
+              .groupby(["field","primary_intent"]).size().reset_index(name="count"))
+        if not fi.empty:
+            pivot = fi.pivot(index="field", columns="primary_intent", values="count").fillna(0)
+            st.plotly_chart(
+                px.imshow(pivot, color_continuous_scale="Blues",
+                          title="CitationHub Field × Intent Distribution Heatmap",
+                          aspect="auto")
+                .update_layout(xaxis_title="Intent", yaxis_title="Field"),
+                use_container_width=True)
+
+    with col_d:
+        st.subheader("Influential Citations (selected paper)")
+        if "is_influential" in seed_events.columns:
+            inf = seed_events["is_influential"].value_counts().reset_index()
+            inf.columns = ["is_influential","count"]
+            inf["label"] = inf["is_influential"].map({True:"Influential", False:"Non-influential"})
+            st.plotly_chart(
+                px.pie(inf, names="label", values="count",
+                       title="Influential vs Non-influential"),
+                use_container_width=True)
+
+    # ── Intent Evolution over Years ────────────────────────────
+    st.markdown("---")
+    st.subheader("CitationHub Intent Evolution over Years")
+    st.caption("How citation intents have changed across all papers over time")
+    intent_trend_raw = (
+        events.dropna(subset=["citing_year"])
+        .assign(year=lambda df: df["citing_year"].astype(int))
+        .query("year >= 2000")
+        .groupby(["year", "primary_intent"]).size()
+        .reset_index(name="count")
+    )
+    if not intent_trend_raw.empty:
+        st.plotly_chart(
+            px.area(
+                intent_trend_raw, x="year", y="count", color="primary_intent",
+                color_discrete_map=INTENT_COLORS,
+                labels={"primary_intent": "Intent", "count": "Citations", "year": "Year"},
+            ).update_layout(
+                legend_title="Intent",
+                xaxis_title="Year", yaxis_title="# Citations",
+                hovermode="x unified",
+            ),
+            use_container_width=True,
+        )
+
+    # ── Top Citing Venues ───────────────────────────────────────
+    st.markdown("---")
+    col_v1, col_v2 = st.columns(2)
+
+    with col_v1:
+        st.subheader("Top Citing Venues")
+        st.caption("Journals/conferences that cite seed papers most")
+        venue_cnt = (
+            events[events["citing_venue"].str.strip() != ""]
+            .groupby("citing_venue").size()
+            .reset_index(name="count")
+            .sort_values("count", ascending=False).head(20)
+        )
+        if not venue_cnt.empty:
+            st.plotly_chart(
+                px.bar(venue_cnt, x="count", y="citing_venue", orientation="h",
+                       labels={"count": "Citations", "citing_venue": ""})
+                .update_layout(yaxis=dict(autorange="reversed"),
+                               xaxis_title="Citations", yaxis_title="", height=520),
+                use_container_width=True,
+            )
+
+    with col_v2:
+        st.subheader("CitationHub Field × Intent Distribution")
+        st.caption("How each field uses citations differently (all fields)")
+        fi_pct = (
+            seed[["seed_paper_id", "field"]]
+            .merge(events[["seed_paper_id", "primary_intent"]], on="seed_paper_id", how="inner")
+            .groupby(["field", "primary_intent"]).size().reset_index(name="count")
+        )
+        if not fi_pct.empty:
+            totals = fi_pct.groupby("field")["count"].transform("sum")
+            fi_pct["pct"] = (fi_pct["count"] / totals * 100).round(1)
+            n_fields = fi_pct["field"].nunique()
+            chart_height = max(520, n_fields * 28)
+            st.plotly_chart(
+                px.bar(fi_pct, x="pct", y="field", color="primary_intent",
+                       orientation="h", color_discrete_map=INTENT_COLORS,
+                       labels={"pct": "% of citations", "field": "", "primary_intent": "Intent"})
+                .update_layout(
+                    barmode="stack",
+                    yaxis=dict(autorange="reversed", categoryorder="total ascending"),
+                    xaxis_title="% of citations", yaxis_title="",
+                    legend_title="Intent", height=chart_height,
+                ),
+                use_container_width=True,
+            )
+
+    # ── Citation Trend over Time ────────────────────────────────
+    st.markdown("---")
+    st.subheader("Citation Trend over Time (selected paper)")
+    st.caption("How citations to the selected seed paper have changed year by year")
+    trend_sel = (seed_events.dropna(subset=["citing_year"])
+                 .assign(citing_year=lambda df: df["citing_year"].astype(int))
+                 .query("citing_year >= 2000")
+                 .groupby("citing_year").size().reset_index(name="count"))
+    if not trend_sel.empty:
+        st.plotly_chart(
+            px.line(trend_sel, x="citing_year", y="count", markers=True,
+                    labels={"citing_year": "Year", "count": "Citations"})
+            .update_layout(xaxis_title="Year", yaxis_title="Citations",
+                           hovermode="x unified"),
+            use_container_width=True)
+    else:
+        st.info("No citation trend data for the selected paper.")
+
+    # ── Export ─────────────────────────────────────────────────
+    st.markdown("---")
+    st.subheader("Export Data")
+    col_e1, col_e2, col_e3 = st.columns(3)
+
+    with col_e1:
+        csv_seed = seed_filtered[
+            ["title", "doi", "journal", "author", "country", "field", "citedby_count"]
+        ].to_csv(index=False).encode("utf-8")
+        st.download_button(
+            "⬇ Seed Papers (CSV)",
+            csv_seed, "seed_papers.csv", "text/csv",
+            use_container_width=True,
+        )
+
+    with col_e2:
+        cite_export = seed_events[
+            ["citing_title", "citing_doi", "citing_year", "citing_venue",
+             "primary_intent", "context_count", "is_influential"]
+        ].rename(columns={
+            "citing_title": "title", "citing_doi": "doi",
+            "citing_year": "year", "citing_venue": "venue",
+            "primary_intent": "intent", "context_count": "contexts",
+            "is_influential": "influential",
+        }).to_csv(index=False).encode("utf-8")
+        st.download_button(
+            "⬇ Citation Events (CSV)",
+            cite_export, "citation_events.csv", "text/csv",
+            use_container_width=True,
+        )
+
+    with col_e3:
+        intent_csv = intent_summary.to_csv(index=False).encode("utf-8")
+        st.download_button(
+            "⬇ Intent Summary (CSV)",
+            intent_csv, "intent_summary.csv", "text/csv",
+            use_container_width=True,
+        )