Multiplex Graph Representation Learning via Bi-level Optimization

Many multiplex graph representation learning (MGRL) methods have been demonstrated to 1) ignore the globally positive and negative relationships among node features; and 2) usually utilize the node classification task to train both graph structure learning and representation learning parameters, and thus resulting in the problem of edge starvation. To address these issues, in this paper, we propose a new MGRL method based on the bi-level optimization. Specifically, in the inner level, we optimize the self-expression matrix to capture the globally positive and negative relationships among nodes, as well as complement them with the local relationships in graph structures. In the outer level, we optimize the parameters of the graph convolutional layer to obtain discriminative node representations. As a result, the graph structure optimization does not depend on the node classification task, which solves the edge starvation problem. Extensive experiments show that our model achieves the superior performance on node classification tasks on all datasets.

Authors

• Yudi Huang Guangxi Key Lab of Multisource Information Mining Security, Guangxi Normal University
• Yujie Mo School of Computer Science and Engineering, University of Electronic Science and Technology of China
• Yujing Liu Guangxi Normal University
• Ci Nie Guangxi Key Lab of Multisource Information Mining Security, Guangxi Normal University
• Guoqiu Wen Guangxi Key Lab of Multisource Information Mining Security, Guangxi Normal University
• Xiaofeng Zhu Guangxi Key Lab of Multisource Information Mining Security, Guangxi Normal University School of Computer Science and Engineering, University of Electronic Science and Technology of China

Keywords

• Data Mining: DM: Mining graphs
• Data Mining: DM: Mining heterogenous data