Homologous nodes in annotated complex networks

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. An- notated networks have typically been used to evaluate community detection approaches. Here, we introduce an approach that combines the quantitative analysis of annotations and network struc- ture, which groups nodes according to similar distributions of node annotations in their neigh- bourhoods. 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.