A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

Jianjun Cheng,Wenbo Zhang,Haijuan Yang,Xing Su,Xiaoyun Chen,Tao Ma

doi:10.1155/2020/9017239

Abstract

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

Highlights

Many complex systems can be epitomized as complex networks, in which vertices represent individuals and edges depict the interrelation of them
To take full advantage of every centrality index, we propose a framework in this paper which integrates multiple centrality indexes by using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) [10] multiattribute decision-making technology to identify seed vertices
E main contributions of our work are as follows: (i) We propose a seed vertices identification framework which integrates multiple centrality indexes using TOPSIS; the seed vertices selected by this method take full advantage of every centrality index (ii) Based on the selected seed vertices, we propose a method to detect communities from networks, which is a seed-expanding method and can detect high-quality community structure without needing to specify the number of communities (iii) Extensive experiments are carried out to testify the effectiveness and performance of the proposed method e remainder of this paper is organized as follows

Summary

Introduction

Many complex systems can be epitomized as complex networks, in which vertices represent individuals and edges depict the interrelation of them. Complex network analysis has been applied in many fields, such as sport competition networks, biological networks, social networks, and political election networks. For these complex networks, community structure is one of their important characteristics. Communities are always corresponding to the functional modules of the real-world systems, such as complexes or pathways in protein-protein interaction networks or metabolic networks, real social groupings with the same occupations, interests, and so forth in social networks. Erefore, we can explore the functional characteristics of the systems via detecting the community structures from the corresponding networks. Some previous studies [2, 3] have shown that networks own some special characteristics at the community level which differ from those at the individual-vertex level or the level of the entire network

Methods

Results

Conclusion