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arXiv Machine Learning By Alberto Acedo

Omega-N: Interpretable Structural Node Descriptors and Their Applicability Domain

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The paper introduces Omega‑N, a set of ten interpretable node‑level structural descriptors derived from localizing four factors of a composite structural index. By correcting the ill‑conditioned localization with a configuration‑null excess and a multi‑scale personalized‑PageRank neighbourhood, Omega‑N achieves competitive or superior performance in six in‑domain node‑classification tasks compared to a recursive feature engine that uses up to 252 features. In drug‑target prioritisation on protein interaction networks, Omega‑N improves AUPRC by 0.073 to 0.144 over a centrality baseline and remains robust across independent datasets and bias controls, though it offers no benefit when combined with Node2Vec. whyItMatters":"The study demonstrates that a compact, interpretable set of structural features can match or exceed more complex feature sets in practical graph‑based prediction tasks, particularly in biomedical network analysis."

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