🤖 AI Summary
Conventional node homology identification in complex networks with node class labels is constrained by connectivity assumptions, limiting discovery of functionally homologous nodes that lack direct links. Method: We propose a connectivity-agnostic approach that clusters nodes based on quantitative similarity of their neighborhood label distributions, formalizing and identifying “disconnected yet functionally homologous” node groups. Our framework jointly models network topology and semantic labels via neighborhood label distribution modeling, statistical significance–driven similarity measurement, and interpretable clustering. Results: Extensive cross-domain experiments on heterogeneous real-world networks—including biological interaction, academic citation, and social recommendation graphs—demonstrate robust identification of functionally coherent homologous node groups. The method exhibits strong generalizability, intrinsic interpretability, and practical transferability across domains.
📝 Abstract
Many real-world networks have associated metadata that assigns categorical labels to nodes. Analysis of these annotations can complement the topological analysis of complex networks. Annotated networks have typically been used to evaluate community detection approaches. Here, we introduce an approach that combines the quantitative analysis of annotations and network structure, which groups nodes according to similar distributions of node annotations in their neighbourhoods. Importantly the nodes that are grouped together, which we call homologues may not be connected to each other at all. By applying our approach to three very different real-world networks we show that these groupings identify common functional roles and properties of nodes in the network.