logbook.json

{
  "schema_version": 1,
  "title": "Repro - MV-FGAD: Towards Efficient and Effective Federated Graph Anomaly Detection via Multi-view Learning",
  "emoji": "\ud83d\udd2c",
  "space_id": "bertfil/yBcY0bY45t",
  "paper": {
    "arxiv_id": "",
    "openreview_id": "yBcY0bY45t",
    "title": "MV-FGAD: Towards Efficient and Effective Federated Graph Anomaly Detection via Multi-view Learning",
    "url": "https://arxiv.org/abs/"
  },
  "tags": [
    "icml2026-repro",
    "paper-yBcY0bY45t"
  ],
  "updated_at": "2026-07-20T03:52:36+00:00",
  "root": {
    "slug": "index",
    "title": "Repro - MV-FGAD: Towards Efficient and Effective Federated Graph Anomaly Detection via Multi-view Learning",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "00-scored-evidence-summary",
        "title": "00 - Scored evidence summary",
        "file": "pages/00-scored-evidence-summary/page.md",
        "children": []
      },
      {
        "slug": "claim-1-mv-fgad-demonstrates-efficiency-and-effectiveness",
        "title": "Claim 1 - MV-FGAD demonstrates efficiency and effectiveness on real-wo\u2026",
        "file": "pages/claim-1-mv-fgad-demonstrates-efficiency-and-effectiveness/page.md",
        "children": []
      },
      {
        "slug": "claim-2-multi-view-learning-mechanism-captures-diverse-ano",
        "title": "Claim 2 - Multi-view learning mechanism captures diverse anomaly patte\u2026",
        "file": "pages/claim-2-multi-view-learning-mechanism-captures-diverse-ano/page.md",
        "children": []
      },
      {
        "slug": "reproduction-protocol-and-provenance",
        "title": "Reproduction protocol and provenance",
        "file": "pages/reproduction-protocol-and-provenance/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
  },
  "agent_view_tokens": 7785,
  "revision": "1784519556959976400"
}