arXiv：cs.LG（机器学习，全量分类）· Jianru Shen·· 14 小时前AI 评分29
组织特异性互作组中的有效电阻与图神经网络可靠性
Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes
AI 导读
研究检验组织特异性互作结构能否指示蛋白质功能预测中哪些结果不可信，候选信号为有效电阻。在 24 个组织特异性互作组中，该信号被度数的倒数主导，随共表达过滤网络增大而退化加深，Spearman 相关系数为 -0.955。
正文
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.
| Comments: | Accepted at IEEE BIBM (Doctoral Forum) |
| Subjects: | Machine Learning (cs.LG); Molecular Networks (q-bio.MN) |
| Cite as: | arXiv:2610.02175 [cs.LG] |
| (or arXiv:2610.02175v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02175 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jianru Shen [view email]
[v1]
Thu, 1 Oct 2026 17:57:10 UTC (140 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org