Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification
Yan Pang, Teng Huang, Zhen Wang, J. Li, Poorya Hosseini, Ji Zhang, Chao Liu, Shan Ai
Abstract
Graph neural networks (GNNs) can be effectively applied to solve many real-world problems across widely diverse fields. Their success is inseparable from the message-passing mechanisms evolving over the years. However, current mechanisms treat all node features equally at the macro-level (node-level), and the optimal aggregation method has not yet been explored. In this paper, we propose a new GNN called Graph Decipher (GD), which transparentizes the message flows of node features from micro-level (feature-level) to global-level and boosts the performance on node classification tasks. Besides, to reduce the computational burden caused by investigating message-passing, only the relevant representative node attributes are extracted by graph feature filters, allowing calculations to be performed in a category-oriented manner. Experiments on 10 node classification data sets show that GD achieves state-of-the-art performance while imposing a substantially lower computational cost. Additionally, since GD has the ability to explore the representative node attributes by category, it can also be applied to imbalanced node classification on multiclass graph data sets.