Abstract
Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we 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 facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.
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Details
1 Institute for Data, Systems, and Society, Massachusetts Institute of Technology, MA, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); ICTEAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium (GRID:grid.7942.8) (ISNI:0000 0001 2294 713X); naXys and Department of Mathematics, University of Namur, Namur, Belgium (GRID:grid.6520.1) (ISNI:0000 0001 2242 8479)
2 ICTEAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium (GRID:grid.7942.8) (ISNI:0000 0001 2294 713X); CORE, Université catholique de Louvain, Louvain-la-Neuve, Belgium (GRID:grid.7942.8) (ISNI:0000 0001 2294 713X)
3 Integrated Science Lab, Department of Physics, Umeå University, Umeå, Sweden (GRID:grid.12650.30) (ISNI:0000 0001 1034 3451)
4 naXys and Department of Mathematics, University of Namur, Namur, Belgium (GRID:grid.6520.1) (ISNI:0000 0001 2242 8479)