Unsupervised Classification for Social Networks with RS Matrix Community detection is an important research issue for understanding the structures of social networks. The existing community detection algorithms can be classified into two major classes: clustering based on modularity and spectral clustering. In this paper, we present a novel method for detecting community structures without prior information such as the number and size of communities in networks. This approach is based on a matrix called RS which enhances the clusterproperties in the data. We reveal the reason why a twolayer GCN is enough to achieve satisfied results. Our partition algorithm divides a network into different communities according to the similarity of the number of mutual neighbours among nodes, and makes use of a silhouette coefficient to determine the final unique partition. Experiments show that our community detection method can obtain accurate community structures on realworld networks, outperforming existing leading algorithms.

Discrete Overlapping Community Detection with Pseudo Supervision Community detection is of significant importance in understanding the structures and functions of networks. Recently, overlapping community detection has drawn much attention due to the ubiquity of overlapping community structures in realworld networks. Nonnegative matrix factorization (NMF), as an emerging standard framework, has been widely employed for overlapping community detection, which obtains nodesu0027 soft community memberships by factorizing the adjacency matrix into lowrank factor matrices. However, in order to determine the ultimate community memberships, we have to postprocess the realvalued factor matrix by manually specifying a threshold on it, which is undoubtedly a difficult task. Even worse, a unified threshold may not be suitable for all nodes. To circumvent the cumbersome postprocessing step, we propose a novel discrete overlapping community detection approach, i.e., Discrete Nonnegative Matrix Factorization (DNMF), which seeks for a discrete (binary) community membership matrix directly. Thus DNMF is able to assign explicit community memberships to nodes without postprocessing. Moreover, DNMF incorporates a pseudo supervision module into it to exploit the discriminative information in an unsupervised manner, which further enhances its robustness. We thoroughly evaluate DNMF using both synthetic and realworld networks. Experiments show that DNMF has the ability to outperform stateoftheart baseline approaches.