Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes

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.

Machine Learning (cs.LG); Molecular Networks (q-bio.MN)

arXiv:2610.02175 [cs.LG]

(or arXiv:2610.02175v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.02175 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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