Multiplex Graph Representation Learning via Bi-level Optimization

Multiplex Graph Representation Learning via Bi-level Optimization

Yudi Huang, Yujie Mo, Yujing Liu, Ci Nie, Guoqiu Wen, Xiaofeng Zhu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 2081-2089. https://doi.org/10.24963/ijcai.2024/230

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.
Keywords:
Data Mining: DM: Mining graphs
Data Mining: DM: Mining heterogenous data