Community Detecting By Signaling on Complex Networks

TL;DR

This paper introduces a signaling-based method for detecting community structures in complex networks by transforming topological relationships into geometric vectors and applying clustering techniques.

Contribution

It proposes a novel community detection approach that uses signaling processes and vector representations, applicable to both weighted and unweighted networks without extra parameters.

Findings

Effective detection of communities in real networks

Comparable or superior results to existing methods

Applicable to both weighted and unweighted networks

Abstract

Based on signaling process on complex networks, a method for identification community structure is proposed. For a network with $n$ nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source once to inspire the whole network by exciting its neighbors and then the source node is endowed a $n$d vector which recording the effects of signaling process. So by this process, the topological relationship of nodes on networks could be transferred into the geometrical structure of vectors in $n$d Euclidian space. Then the best partition of groups is determined by $F$-statistic and the final community structure is given by Fuzzy $C$-means clustering method (FCM). This method can detect community structure both in unweighted and weighted networks without any extra parameters. It has been applied to ad hoc networks and some real networks including Zachary Karate Club network and football team network. The results are compared with that of other approaches and the evidence indicates that the algorithm based on signaling process is effective.