Community detection in networks: A user guide
arXiv:1608.00163 · doi:10.1016/j.physrep.2016.09.002
Abstract
Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.
43 pages, 29 figures, 2 tables, 202 references. Final version published in Physics Reports
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Cited by in corpus (11)
- Nestedness in complex networks: Observation, emergence, and implications
- The many facets of community detection in complex networks
- Nonparametric weighted stochastic block models
- Efficient method for estimating the number of communities in a network
- Post-processing partitions to identify domains of modularity optimization
- Layer Communities in Multiplex Networks
- Hierarchical benchmark graphs for testing community detection algorithms
- Fast Heuristic Algorithm for Multi-scale Hierarchical Community Detection
- Modularity in Multilayer Networks using Redundancy-based Resolution and Projection-based Inter-Layer Coupling
- Entrograms and coarse graining of dynamics on complex networks
- Topological aspects of the multi-language phases of the Naming Game on community-based networks