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 …

Date

August 29, 2009

Authors

Rumi Ghosh, Kristina Lerman

Conference

2009 International Conference on Computational Science and Engineering

Volume

4

Pages

98-105

Publisher

IEEE