Title

Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

Abstract

A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows one to study community structure in a very general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales. As an example application of this methodology, we identify communities in similarity networks in the historical roll call record of the U.S. Senate.

The accepted version of the manuscript is linked here. The definitive version of this work was published in Science 328, 876-878, May 14, 2010 (DOI:10.1126/science.1184819).

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