A Unified Community Detection, Visualization and Analysis method

TL;DR

This paper introduces a unified method for community detection that handles unipartite, bipartite, directed, and overlapping graphs, providing visualization tools for semantic analysis across social, biological, and neurological data.

Contribution

It presents a novel unified approach that simultaneously detects communities across different node types and merges partitioned and overlapping communities, with enhanced visualization capabilities.

Findings

Successfully applied to social network datasets revealing social structures.

Produced biologically meaningful clusters in brain tractography data.

Enhanced visualization aids in semantic interpretation of communities.

Abstract

Community detection in social graphs has attracted researchers' interest for a long time. With the widespread of social networks on the Internet it has recently become an important research domain. Most contributions focus upon the definition of algorithms for optimizing the so-called modularity function. In the first place interest was limited to unipartite graph inputs and partitioned community outputs. Recently bipartite graphs, directed graphs and overlapping communities have been investigated. Few contributions embrace at the same time the three types of nodes. In this paper we present a method which unifies commmunity detection for the three types of nodes and at the same time merges partitionned and overlapping communities. Moreover results are visualized in such a way that they can be analyzed and semantically interpreted. For validation we experiment this method on well known simple benchmarks. It is then applied to real data in three cases. In two examples of photos sets with tagged people we reveal social networks. A second type of application is of particularly interest. After applying our method to Human Brain Tractography Data provided by a team of neurologists, we produce clusters of white fibers in accordance with other well known clustering methods. Moreover our approach for visualizing overlapping clusters allows better understanding of the results by the neurologist team. These last results open up the possibility of applying community detection methods in other domains such as data analysis with original enhanced performances.