Co-Association Matrix-Based Multi-Layer Fusion for Community Detection in Attributed Networks.

Sheng Luo,Shuwen Ma,Zhifei Zhang,Yuanjian Zhang

doi:10.3390/e21010095

Abstract

Community detection is a challenging task in attributed networks, due to the data inconsistency between network topological structure and node attributes. The problem of how to effectively and robustly fuse multi-source heterogeneous data plays an important role in community detection algorithms. Although some algorithms taking both topological structure and node attributes into account have been proposed in recent years, the fusion strategy is simple and usually adopts the linear combination method. As a consequence of this, the detected community structure is vulnerable to small variations of the input data. In order to overcome this challenge, we develop a novel two-layer representation to capture the latent knowledge from both topological structure and node attributes in attributed networks. Then, we propose a weighted co-association matrix-based fusion algorithm (WCMFA) to detect the inherent community structure in attributed networks by using multi-layer fusion strategies. It extends the community detection method from a single-view to a multi-view style, which is consistent with the thinking model of human beings. Experiments show that our method is superior to the state-of-the-art community detection algorithms for attributed networks.

Highlights

A large number of complex systems in the real world are often represented as complex networks, such as communication systems, biological systems, social systems, traffic systems and World WideWeb (WWW), etc. [1,2]
If the distribution q of α is not able to approximate p, the uncertainty of the system will increase, that is, the cross entropy H ( p, q) will become larger, which affects the stability and accuracy of the community detection algorithms. These methods could work well in the attributed graph, they still remain some problems such as loss of information, uncertainties for weighting parameter, results depending on expert experience, etc.; in particular, the fusion strategy is a big challenge to mix the multi-source heterogeneous data. We summarize these methods as the community detection algorithm fusing at a lower-layer
The experiments proved that our method is effective and outperforms state-of-the-art community detection algorithms for attributed networks

Summary

Introduction

A large number of complex systems in the real world are often represented as complex networks, such as communication systems, biological systems, social systems, traffic systems and World WideWeb (WWW), etc. [1,2]. A large number of complex systems in the real world are often represented as complex networks, such as communication systems, biological systems, social systems, traffic systems and World Wide. One of the most important characteristics of complex networks is community structure [3]. Detecting community structure is one of the fundamental problems in complex network analysis. By detecting the community structure, one could understand the intrinsic characteristics of complex networks, and its evolutionary trends. The community detection problem has become one of the hot spots in the field of complex network analysis. Many experts and scholars have proposed a large number of excellent algorithms to find the community structure

Methods

Results

Conclusion