import gradio as gr
import pandas as pd
import networkx as nx
from pyvis.network import Network
import tempfile
import os
def calculate_centralities(df, is_directed):
# Build NetworkX graph
G = nx.from_pandas_edgelist(df, 'Source', 'Target', create_using=nx.DiGraph() if is_directed else nx.Graph())
# Calculate centralities
deg_cent = nx.degree_centrality(G)
bet_cent = nx.betweenness_centrality(G)
# Eigenvector has a fallback for non-convergence or directed graphs
try:
eig_cent = nx.eigenvector_centrality(G, max_iter=1000)
except:
try:
eig_cent = nx.eigenvector_centrality_numpy(G)
except:
eig_cent = {node: 0.0 for node in G.nodes()}
clo_cent = nx.closeness_centrality(G)
# Build table
records = []
for node in G.nodes():
records.append({
"Node": node,
"Degree Centrality": deg_cent.get(node, 0.0),
"Betweenness Centrality": bet_cent.get(node, 0.0),
"Eigenvector Centrality": eig_cent.get(node, 0.0),
"Closeness Centrality": clo_cent.get(node, 0.0)
})
df_cent = pd.DataFrame(records).sort_values("Degree Centrality", ascending=False)
return G, df_cent
def get_color_gradient(value, max_val):
# Maps centrality value to an aesthetic gradient: low = muted brown, high = hot orange/white
if max_val <= 0:
return "#ff7043"
ratio = min(value / max_val, 1.0)
# Interpolate colors between #3d281c (wash) and #ff7043 (accent) or #ffffff
r = int(61 + (255 - 61) * ratio)
g = int(40 + (112 - 40) * ratio)
b = int(28 + (67 - 28) * ratio)
return f"#{r:02x}{g:02x}{b:02x}"
def generate_vis_html(G, df_cent, active_metric):
net = Network(
height="500px",
width="100%",
bgcolor="#16100c",
font_color="#f4eee6",
notebook=False
)
net.set_options("""
var options = {
"nodes": {
"borderWidth": 2,
"font": {
"color": "#f4eee6",
"size": 14,
"face": "Inter, sans-serif"
}
},
"edges": {
"color": {
"color": "rgba(255, 112, 67, 0.25)",
"highlight": "#ff7043"
},
"smooth": {
"type": "continuous"
}
},
"physics": {
"barnesHut": {
"gravitationalConstant": -12000,
"centralGravity": 0.3,
"springLength": 120,
"springConstant": 0.04
}
}
}
""")
# Score dictionary
scores = dict(zip(df_cent['Node'], df_cent[active_metric]))
max_score = max(scores.values()) if scores else 1.0
for node in G.nodes():
score = scores.get(node, 0.0)
# Sizing logic: baseline = 10, scaled up to max 45
size = 10 + (35 * (score / max_score if max_score > 0 else 0))
color = get_color_gradient(score, max_score)
net.add_node(
node,
label=node,
size=size,
color=color,
title=f"Centrality Score: {score:.5f}"
)
# Add edges
for edge in G.edges():
net.add_edge(edge[0], edge[1])
temp_dir = tempfile.gettempdir()
temp_path = os.path.join(temp_dir, next(tempfile._get_candidate_names()) + ".html")
net.save_graph(temp_path)
with open(temp_path, "r", encoding="utf-8") as f:
html_content = f.read()
try:
os.remove(temp_path)
except:
pass
escaped_html = html_content.replace('"', '"')
iframe_code = f''
return iframe_code
def analyze_centrality(file_obj, is_directed, active_metric):
if file_obj is None:
return "Please upload a CSV or Excel network dataset.", "", None, None, None
try:
if file_obj.name.endswith('.csv'):
df = pd.read_csv(file_obj.name)
else:
df = pd.read_excel(file_obj.name)
except Exception as e:
return f"Error reading file: {str(e)}", "", None, None, None
# Standardize column headers
rename_map = {}
for col in df.columns:
if col.lower() in ['source', 'from', 'node1']:
rename_map[col] = 'Source'
elif col.lower() in ['target', 'to', 'node2']:
rename_map[col] = 'Target'
df = df.rename(columns=rename_map)
if 'Source' not in df.columns or 'Target' not in df.columns:
return "CSV/Excel must contain at least 'Source' and 'Target' columns representing network edges.", "", None, None, None
# Calculate scores
G, df_cent = calculate_centralities(df, is_directed)
# General stats
stats_html = f"""
Network Nodes
{G.number_of_nodes()}
Network Edges
{G.number_of_edges()}
"""
# Generate PyVis HTML
vis_html = generate_vis_html(G, df_cent, active_metric)
# Sort for displaying
display_df = df_cent.sort_values(active_metric, ascending=False)
# Download scores CSV
out_csv = tempfile.mktemp(suffix=".csv")
df_cent.to_csv(out_csv, index=False)
return "", stats_html, vis_html, display_df, gr.update(value=out_csv, visible=True)
theme = gr.themes.Default(
primary_hue="orange",
neutral_hue="stone"
).set(
body_background_fill="#0d0907",
body_text_color="#c4bbae",
block_background_fill="#16100c",
block_border_width="1px",
block_label_text_color="#f4eee6"
)
with gr.Blocks(theme=theme, title="Centrality Analysis") as demo:
gr.Markdown(
"""
# 👑 Network Centrality Analysis Suite
### Quantify node influence and structural power inside complex networks using four classical centrality algorithms. Drag, zoom, and visualize node importance dynamically!
"""
)
error_msg = gr.Markdown("", visible=False)
with gr.Row():
with gr.Column(scale=1):
file_obj = gr.File(label="Upload CSV or Excel Network File", file_types=[".csv", ".xlsx"])
is_directed = gr.Checkbox(label="Is Directed Network", value=False)
active_metric = gr.Radio(
choices=["Degree Centrality", "Betweenness Centrality", "Eigenvector Centrality", "Closeness Centrality"],
value="Degree Centrality",
label="Centrality Measure",
info="Degree (total links), Betweenness (brokerage), Eigenvector (influence of connections), Closeness (distance)."
)
btn = gr.Button("Calculate Centrality Rankings", variant="primary")
with gr.Column(scale=2):
stats_box = gr.HTML()
with gr.Tabs():
with gr.TabItem("Interactive Graph Scaling"):
vis_box = gr.HTML()
with gr.TabItem("Rankings Table"):
table_box = gr.Dataframe(headers=["Node", "Degree Centrality", "Betweenness Centrality", "Eigenvector Centrality", "Closeness Centrality"])
download_btn = gr.File(label="Download Calculated Rankings CSV", visible=False)
def process(file_obj, is_directed, metric):
err, stats, vis, table, csv_path = analyze_centrality(file_obj, is_directed, metric)
if err:
return gr.update(value=err, visible=True), "", "", None, gr.update(visible=False)
return gr.update(visible=False), stats, vis, table, csv_path
btn.click(
process,
inputs=[file_obj, is_directed, active_metric],
outputs=[error_msg, stats_box, vis_box, table_box, download_btn]
)
if __name__ == "__main__":
demo.launch()