保留模体信息的属性二分图神经网络表示学习OA北大核心CSTPCD

At present,network representation learning methods are mostly aimed at homogeneous networks,ignoring the particularity of attributed bipartite networks and the motifs structure of networks.In order to solve the above problems,this paper proposes an attributed bipartite graph neural network with motifs information for network representation learning(MABG).MABG adjusts the edge weights by the number of butterfly motifs formed by two nodes in the network,to con-struct the motifs weight matrix and obtain the attributed bipartite network adjacency matrix with motifs information.Then two different strategies are adopted to capture the explicit and implicit messages in the bipartite network.For explicit relationships,a message-passing mechanism is operated between different types of nodes.For implicit relationships,a message alignment mechanism is used in nodes of the same type.An adversarial model is implemented to minimize the difference between input attributes and explicit relationship representations.Finally,a cascaded framework is proposed to capture high-order network information and obtain the final node representation.Extensive experiments are conducted in recommended tasks on four real-world datasets.The results demonstrate the effectiveness of MABG compared with other state-of-art methods.