Exploring the Role of Node Diversity in Directed Graph Representation Learning
Exploring the Role of Node Diversity in Directed Graph Representation Learning
Jincheng Huang, Yujie Mo, Ping Hu, Xiaoshuang Shi, Shangbo Yuan, Zeyu Zhang, Xiaofeng Zhu
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 2072-2080.
https://doi.org/10.24963/ijcai.2024/229
Many methods of Directed Graph Neural Networks (DGNNs) are designed to equally treat nodes in the same neighbor set (i.e., out-neighbor set and in-neighbor set) for every node, without considering the node diversity in directed graphs, so they are often unavailable to adaptively acquire suitable information from neighbors of different directions. To alleviate this issue, in this paper, we investigate a new way to first consider node diversity for representation learning on directed graphs, i.e., neighbor diversity and degree diversity, and then propose a new NDDGNN framework to adaptively assign weights to both outgoing information and incoming information at the node level. Extensive experiments on seven real-world datasets validate the superior performance of our method compared to state-of-the-art methods in terms of both node classification and link prediction tasks.
Keywords:
Data Mining: DM: Mining graphs
Machine Learning: ML: Representation learning
Machine Learning: ML: Semi-supervised learning