Homologous nodes in annotated complex networks

TL;DR

This paper introduces a novel method for analyzing annotated complex networks by grouping nodes with similar annotation distributions, revealing functional roles even among unconnected nodes.

Contribution

The paper presents a new approach that combines annotation and network structure analysis to identify homologous nodes with similar roles.

Findings

Groupings reveal common functional roles of nodes.

Method identifies homologous nodes regardless of connectivity.

Applicable to diverse real-world networks.

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.