SBiNE: Signed Bipartite Network Embedding

Abstract

This work develops a representation learning method for signed bipartite networks. Recent years, embedding nodes of a given network into a low dimensional space has attracted much interest due to it can be widely applied in link prediction, clustering, and anomalous detection. Most existing network embedding methods mainly focus on homogeneous networks with only positive edges and single node type. However, negative edges are more valuable than positive edges in certain analysis tasks. Even though the work on signed network representation learning distinguishes between positive and negative edges, it does not consider the difference in node types. Moreover, bipartite network representation learning which considers two types of vertices do not tell link signs. In order to solve this problem, we further consider the link sign on the basis of the bipartite network to conduct signed bipartite network analysis. In this paper, we propose a simple deep learning framework SBiNE, short for signed bipartite network embedding, which both preserves the first-order (i.e., observed links) and second-order proximity (i.e., unobserved links but have similar sign context), and then by optimizing the objective function, experiments on three datasets show that our proposed framework SBiNE is competitive in link sign prediction task.