4Different Approaches to Community Detection

Martin Rosvall1, Jean-Charles Delvenne2, Michael T. Schaub3 and Renaud Lambiotte4

1Umeå University

2Université Catholique de Louvain

3Massachusetts Institute of Technology

4University of Oxford

This chapter is an extended version of The many facets of community detection in complex networks, Appl. Netw. Sci. 2: 4 (2017) by the same authors.

4.1 Introduction

A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.

While research related to community detection dates back to the 1970s in mathematical sociology and circuit design [46,21], Newman's and Girvan's work ...

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