Community detection in graphs

Details Community detection in graphs Community detection in graphs

2009-06-03

arxiv.org/abs/0906.0612v2

Physics Reports 486, 75-174 (2010)

Physics

Physics and Society

Review article. 103 pages, 42 figures, 2 tables. Two sections expanded + minor modifications. Three figures + one table + refe

Scientific paper

10.1016/j.physrep.2009.11.002

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.

Affiliated with

Also associated with

No associations

Rating

Community detection in graphs does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Community detection in graphs, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Community detection in graphs will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-193399