Eb&D: A new clustering approach for signed social networks based on both edge-betweenness centrality and density of subgraphs

• Published in: Physica A Vol. 482; pp. 147 - 157
• Main Authors: Qi, Xingqin, Song, Huimin, Wu, Jianliang, Fuller, Edgar, Luo, Rong, Zhang, Cun-Quan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15-09-2017
• Subjects: Clustering; Density; Edge-betweenness; Signed network; Signed network; Clustering; Edge-betweenness; Density
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2017.04.037

Clustering algorithms for unsigned social networks which have only positive edges have been studied intensively. However, when a network has like/dislike, love/hate, respect/disrespect, or trust/distrust relationships, unsigned social networks with only positive edges are inadequate. Thus we model such kind of networks as signed networks which can have both negative and positive edges. Detecting the cluster structures of signed networks is much harder than for unsigned networks, because it not only requires that positive edges within clusters are as many as possible, but also requires that negative edges between clusters are as many as possible. Currently, we have few clustering algorithms for signed networks, and most of them requires the number of final clusters as an input while it is actually hard to predict beforehand. In this paper, we will propose a novel clustering algorithm called Eb&D for signed networks, where both the betweenness of edges and the density of subgraphs are used to detect cluster structures. A hierarchically nested system will be constructed to illustrate the inclusion relationships of clusters. To show the validity and efficiency of Eb&D, we test it on several classical social networks and also hundreds of synthetic data sets, and all obtain better results compared with other methods. The biggest advantage of Eb&D compared with other methods is that the number of clusters do not need to be known prior.
•A clustering method is presented for signed networks.•This method is a graph theory based approach.•It does not need the number of clusters in advance.•It performs better than other existing method with applying on several data sets.