Community Detection in Signed Networks based on the Signed Stochastic Block Model and exact ICL

There has been an increasing interest in detecting community in signed networks because signed networks contain more information (both positive and negative edges) than unsigned networks (only positive edges). Many methods have been proposed to find communities in signed networks; however, most of them can be regarded as the discriminative methods that do not concern with how the signed networks are generated so that they are usually difficult to characterize accurately the intrinsic community structure. The existing method SSL which is based on a generative model can achieve high accuracy in community detection in signed networks. However, SSL needs to estimate the models in the possible space one by one, which requires a large amount of calculation. In view of this, we propose a method to find community in signed networks, in which the exact integrated complete data likelihood (ICLex) for the signed stochastic block model proposed in SSL is derived and a greedy search is employed to optimize the value of the derived ICLex for signed networks to find communities. Our method has a rigorous probabilistic interpretation and does not need to estimate the models one by one from the possible space. The knowledge on hyper-parameters of our model is not necessary. In the experiments, the proposed method is tested on the synthetic and real-world networks and compared with several current methods. The experimental results show that our method can find the communities in signed networks more accurately than these current methods and more efficiently than SSL.