Community detection in graphs

@article{Fortunato2009CommunityDI,
  title={Community detection in graphs},
  author={Santo Fortunato},
  journal={ArXiv},
  year={2009},
  volume={abs/0906.0612}
}

Community Detection in Complex Networks via Clique Conductance

This paper develops a novel community-detection method based on cliques, i.e., local complete subnetworks, and shows that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.

Communities Identification Using Nodes Features

The main purpose of the approach is to find leader nodes of networks and to form community around those nodes and the proposed algorithm doesn’t require a priori knowledge of k number of communities to be detected.

Graph Clustering Based on Social Network Community Detection Algorithms

The so-called walktrap algorithm aims to detect communities in graphs based on the idea that random walks tend to get trapped within communities within communities (areas with higher density of links and separated by few connections).

Finding Statistically Significant Communities in Networks

OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.

Detection of structurally homogeneous subsets in graphs

Methods for detecting communities in undirected graphs have been recently reviewed by Fortunato and a review of methods and algorithms for detecting essentially structurally homogeneous subsets of vertices in binary or weighted and directed and undirecting graphs is made.

Detection of structurally homogeneous subsets in graphs

Methods for detecting communities in undirected graphs have been recently reviewed by Fortunato and a review of methods and algorithms for detecting essentially structurally homogeneous subsets of vertices in binary or weighted and directed and undirecting graphs is made.

A Survey on Community Detection

It has been proved that many real world networks reveal the structures of the modules or the communities that are sub graphs with more edges connecting the vertices of the same group and comparatively fewer links joining the outside vertices.

Genetic Algorithms Approach to Community Detection

There are many versions of genetic algorithms developed for the task of community detection and here the authors concentrate on a very promising one proposed quite recently by Pizzuti, namely on genetic algorithms.
...

References

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This paper employs approximation algorithms for the graph-partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities, and defines the network community profile plot, which characterizes the "best" possible community—according to the conductance measure—over a wide range of size scales.

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