Abstract

Real-world networks are often organized as modules or communities of similar nodes that serve as functional units. These networks are also rich in content, with nodes having distinguished features or attributes. In order to discover a network’s modular structure, it is necessary to take into account not only its links but also node attributes. We describe an information-theoretic method that identifies modules by compressing descriptions of information flow on a network. Our formulation introduces node content into the description of information flow, which we then minimize to discover groups of nodes with similar attributes that also tend to trap the flow of information. The method is conceptually simple and does not require ad-hoc parameters to specify the number of modules or to control the relative contribution of links and node attributes to network structure. We apply the proposed method to partition real-world …

Date

November 19, 2016

Authors

Laura M Smith, Linhong Zhu, Kristina Lerman, Allon G Percus

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)

Volume

11

Issue

2

Pages

1-26

Publisher

ACM