Structure of Heterogeneous Networks

TL;DR

This paper introduces a mathematical framework for analyzing heterogeneous networks, extending centrality and community detection methods to preserve multi-entity relationships and reveal new structural insights.

Contribution

It generalizes Bonacich centrality and modularity-maximization to heterogeneous networks, enabling more accurate analysis of complex multi-entity systems.

Findings

New insights into network structure from multi-entity link analysis

Tunable parameter reveals important nodes at different interaction scales

Enhanced community detection considering diverse entity types

Abstract

Heterogeneous networks play a key role in the evolution of communities and the decisions individuals make. These networks link different types of entities, for example, people and the events they attend. Network analysis algorithms usually project such networks unto simple graphs composed of entities of a single type. In the process, they conflate relations between entities of different types and loose important structural information. We develop a mathematical framework that can be used to compactly represent and analyze heterogeneous networks that combine multiple entity and link types. We generalize Bonacich centrality, which measures connectivity between nodes by the number of paths between them, to heterogeneous networks and use this measure to study network structure. Specifically, we extend the popular modularity-maximization method for community detection to use this centrality metric. We also rank nodes based on their connectivity to other nodes. One advantage of this centrality metric is that it has a tunable parameter we can use to set the length scale of interactions. By studying how rankings change with this parameter allows us to identify important nodes in the network. We apply the proposed method to analyze the structure of several heterogeneous networks. We show that exploiting additional sources of evidence corresponding to links between, as well as among, different entity types yields new insights into network structure.