A Classification for Community Discovery Methods in Complex Networks

TL;DR

This paper reviews various community discovery methods in complex networks, categorizing them based on their definitions of communities and providing guidance for selecting suitable approaches for different network features.

Contribution

It offers a comprehensive classification of community detection methods based on their community definitions, aiding researchers in choosing appropriate algorithms.

Findings

Organized community detection methods into categories based on community definitions

Provided a manual for selecting methods according to network features

Highlighted the diversity of community definitions in the literature

Abstract

In the last few years many real-world networks have been found to show a so-called community structure organization. Much effort has been devoted in the literature to develop methods and algorithms that can efficiently highlight this hidden structure of the network, traditionally by partitioning the graph. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition it then extracts the communities that are able to reflect only some of the features of real communities. The aim of this survey is to provide a manual for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery based on their own definition of community. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on.