2009/06/03 by Santo Fortunato · 2 voices · 31 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Opinion Dynamics and Social Influence #cond-mat.stat-mech #cs.IR #physics.bio-ph #physics.comp-ph #physics.soc-ph #q-bio.QM
paper · pdf · doi:10.1016/j.physrep.2009.11.002
arxiv published 2009/06/03 · openalex publication_date 2009/11/18 · arxiv updated 2010/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.