Community Detecting By Signaling on Complex 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.

14 pages, 8 figures

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• Optimal Tree for Both Synchronizability and Converging Time
• A similarity-based community detection method with multiple prototype representation
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• Detecting the optimal number of communities in complex networks
• Membership in social networks and the application in information filtering
• Evidential relational clustering using medoids
• Impact of Dynamic Interactions on Multi-Scale Analysis of Community Structure in Networks
• Evaluating community structure in large network with random walks