Parallel clustering with CFinder

TL;DR

This paper introduces a parallel version of CFinder, enabling the analysis of large networks by distributing community detection tasks across multiple processors, thus making large-scale network community structures more accessible.

Contribution

The paper presents a grid-based parallel implementation of CFinder for detecting overlapping communities in large networks using the clique percolation method.

Findings

Parallel CFinder can handle larger networks than the original.

Distribution of computation enables analysis of extremely large networks.

Parallel implementation makes community detection more accessible for big data.

Abstract

The amount of available data about complex systems is increasing every year, measurements of larger and larger systems are collected and recorded. A natural representation of such data is given by networks, whose size is following the size of the original system. The current trend of multiple cores in computing infrastructures call for a parallel reimplementation of earlier methods. Here we present the grid version of CFinder, which can locate overlapping communities in directed, weighted or undirected networks based on the clique percolation method (CPM). We show that the computation of the communities can be distributed among several CPU-s or computers. Although switching to the parallel version not necessarily leads to gain in computing time, it definitely makes the community structure of extremely large networks accessible.