Reconstructing community structure of online social network via user opinions

Abstract

User opinion affects the performance of network reconstruction greatly since it plays a crucial role in the network structure. In this paper, we present a novel model for reconstructing the social network with community structure by taking into account the Hegselmann–Krause bounded confidence model of opinion dynamic and compressive sensing method of network reconstruction. Three types of user opinion, including the random opinion, the polarity opinion, and the overlap opinion, are constructed. First, in Zachary’s karate club network, the reconstruction accuracies are compared among three types of opinions. Second, the synthetic networks, generated by the Stochastic Block Model, are further examined. The experimental results show that the user opinions play a more important role than the community structure for the network reconstruction. Moreover, the polarity of opinions can increase the accuracy of inter-community and the overlap of opinions can improve the reconstruction accuracy of intra-community. This work helps reveal the mechanism between information propagation and social relation prediction.