A multi-similarity spectral clustering method for community detection in dynamic networks.

Xuanmei Qin,Ning Yuan,Pengfei Jiao,Weidi Dai,Wenjun Wang

doi:10.1038/srep31454

Abstract

Community structure is one of the fundamental characteristics of complex networks. Many methods have been proposed for community detection. However, most of these methods are designed for static networks and are not suitable for dynamic networks that evolve over time. Recently, the evolutionary clustering framework was proposed for clustering dynamic data, and it can also be used for community detection in dynamic networks. In this paper, a multi-similarity spectral (MSSC) method is proposed as an improvement to the former evolutionary clustering method. To detect the community structure in dynamic networks, our method considers the different similarity metrics of networks. First, multiple similarity matrices are constructed for each snapshot of dynamic networks. Then, a dynamic co-training algorithm is proposed by bootstrapping the clustering of different similarity measures. Compared with a number of baseline models, the experimental results show that the proposed MSSC method has better performance on some widely used synthetic and real-world datasets with ground-truth community structure that change over time.

Highlights

Community structure is one of the fundamental characteristics of complex networks
The existing evolutionary clustering methods that are most similar to multi-similarity spectral (MSSC) are the preserving cluster quality method (PCQ) and preserving cluster membership method (PCM) methods[26]
The NMI is a well known entropy measure in information theory, which measures the similarity of two clusters

Summary

Introduction

Community structure is one of the fundamental characteristics of complex networks. Many methods have been proposed for community detection. The evolutionary clustering framework was proposed for clustering dynamic data, and it can be used for community detection in dynamic networks. To detect the community structure in dynamic networks, our method considers the different similarity metrics of networks. The communities are extracted at a given snapshot while ignoring the changing trends among and within communities of the dynamic networks These two-stage methods are extremely noise-sensitive and produce unstable clustering results. A better choice is to consider multiple time steps as a whole and the evolutionary clustering algorithm is proposed[23], which can detect communities of the current snapshot by joining with the community structure of the previous snapshot. Evolutionary clustering algorithm enables one to detect current communities using community structures from the previous steps by introducing an item called the temporal smoothness. PCQ and PCM are two proposed frameworks that www.nature.com/scientificreports/

Objectives

Methods

Results

Conclusion