Abstract
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.
Links and resources
Tags
community