文章基本信息

标题：A multi-similarity spectral clustering method for community detection in dynamic networks
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作者：Xuanmei Qin ; Weidi Dai ; Pengfei Jiao 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2016
卷号：6
期号：1
DOI：10.1038/srep31454
语种：English
出版社：Springer Nature
摘要：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.