Bipartite network embedding with Symmetric Neighborhood Convolution

Mining bipartite networks, a ubiquitous type of model particularly developed for networks with two different classes of nodes, is of great importance in various applications. Recently, the advance of network representation learning has provided more tractable, effective and robust methods for several graph mining tasks. However, the strength of representation learning has not been sufficiently explored for bipartite structure, since that most existing learning methods either separately treat the nodes in different classes by defective projections, or independently sample instances for learning by adopting relation-aware random walks. Both methods can lose information and they hardly leverage the symmetry property of bipartite structure directly. In this paper, a novel method called Symmetric Neighborhood Convolution is proposed to fulfill the representation learning task in bipartite networks. The method takes into consideration the topological symmetry property and utilizes one-dimensional convolution kernels to extract features from neighborhood for the nodes in each class. Three different objectives are designated to compare the performance of the proposed convolution-based mechanism. The method is evaluated on five real-world datasets by the tasks of link prediction and recommendation. Comparing to eight competitive baselines, experimental results demonstrate the effectiveness of our method. •A novel neural network-based method for bipartite network embedding.•Use symmetric one-dimensional convolution kernels for feature learning.•The method can accommodate the heterogeneity in bipartite structure.•Extensive experiments manifest the superiority of the proposed method.