{
  "abstract": "Protein function annotation needs to know which predictions to distrust, not only what a model predicts. We ask whether tissue-specific interaction structure carries that information. Our candidate signal is effective resistance, used previously to relieve over-squashing by rewiring. Across 24 tissue-specific interactomes it is dominated by inverse degree, and the degeneration deepens as the co-expression filtered network grows, with a Spearman correlation of -0.955. The residual departure from that limit exceeds degree-preserving null graphs in all 24 networks. Controlling for predictive entropy, degree, annotation cardinality, local structure and feature-only difficulty, the residual explains additional per-node loss in 19 of 24 held-out networks once a permutation floor is subtracted, at every depth, and the effect strengthens monotonically with depth. The increment reaches 0.37% of the variance the controls leave unexplained, 5.6 times a permutation floor, against 1.5 times when the model is retrained in a degree-preserving null world. Selective prediction improves negligibly. The signal is reproducible; degree degeneration bounds it.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.02175",
  "author_total": 1,
  "authors": [
    {
      "name": "Jianru Shen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/78jm7k16t20cx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.02175",
  "fulltext_url": null,
  "id": "78jm7k16t20cx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.02175",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.MN"
    ],
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7216071116",
  "pdf_url": "https://arxiv.org/pdf/2610.02175",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/78jm7k16t20cx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:08Z",
      "native_id": "2610.02175",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.02175"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.MN",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes",
  "title_text": "Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:08Z",
  "url": "https://research.draconitic.com/paper/78jm7k16t20cx",
  "venue": "arXiv",
  "versions": []
}