Detecting community structures in networks by label propagation with prediction of percolation transition.

Aiping Zhang,Baozhu Jia,Shubin Zhang,Guang Ren,Yejin Lin,Hui Cao,Jundong Zhang

doi:10.1155/2014/148686

Abstract

Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. We propose a label propagation algorithm with prediction of percolation transition (LPAp). After analyzing the reason for multiple solutions of LPA, by transforming the process of community detection into network construction process, a trivial solution in label propagation is considered as a giant component in the percolation transition. We add a prediction process of percolation transition in label propagation to delay the occurrence of trivial solutions, which makes small communities easier to be found. We also give an incomplete update condition which considers both neighbor purity and the contribution of small degree vertices to community detection to reduce the computation time of LPAp. Numerical tests are conducted. Experimental results on synthetic networks and real-world networks show that the LPAp is more accurate, more sensitive to small community, and has the ability to identify a single community structure. Moreover, LPAp with the incomplete update process can use less computation time than LPA, nearly without modularity loss.

Highlights

A complex system from nature, society, or any other field can usually be represented as a complex network: a structure with vertices and edges between vertices [1,2,3,4,5,6,7,8,9]
More and more algorithms are proposed and developed to detect the community structure, especially in recent years, such as Girvan-Newman algorithm (GN) [2], spectral clustering [4], spin-glass model [5], the algorithm proposed by Clauset, Newman, and Moore (CNM) [6, 7], partition method using integrating attributes of vertices [8], and extremal optimization [9]
We propose LPAp by adding the prediction process of percolation transition and introduce the incomplete condition in label propagation process to reduce the running time

Summary

Introduction

A complex system from nature, society, or any other field can usually be represented as a complex network: a structure with vertices and edges between vertices [1,2,3,4,5,6,7,8,9]. Due to the randomness in label propagation, when LPA is used to detect the communities in a network, any information about this network except its vertices and edges need not be provided, and the multiple community structures usually are obtained. The obtained community structure using LPA-δ proposed by Leung et al is still scale-independent because the algorithm does not involve modularity optimization [12]. Leung et al provide an idea to save the running time by avoiding label update of those vertices with high neighbor purity [12] It does do well in saving time while not doing well in accuracy because the neighbor purity condition ignores contribution of the small degree vertices to community detection. The LPAp is more accurate and can be faster than the original algorithm

Problem Description

Prediction Process of Percolation Transition

Incomplete Update Condition

Algorithm Description

Experiments

Conclusions