PAMT: A Novel Propagation-Based Approach via Adaptive Similarity Mask for Node Classification

Jinsong Chen, Boyu Li, Qiuting He, Kun He

Abstract: Semisupervised node classification on attributed networks is a crucial task for network analysis. By decoupling two critical operations in graph convolutional networks (GCNs), namely feature transformation and neighborhood aggregation, recent works of decoupled GCNs could support the information to propagate deeper and achieve advanced performance on node classification. However, they follow the structure-aware propagation strategy of GCNs, making it hard to capture the attribute correlation of nodes and be sensitive to the structure noise described by edges whose two endpoints belong to different categories. To address these issues, we propose a new method called the propagation with adaptive mask then training (PAMT). The key idea is to integrate the attribute similarity mask into the structure-aware propagation process. In this way, PAMT could preserve the attribute correlation of adjacent nodes during the propagation and effectively reduce the influence of structure noise. Moreover, we develop an iterative refinement mechanism to update the similarity mask during the training process to improve the training performance. Extensive experiments on six real-world datasets demonstrate the superior performance and robustness of PAMT over the state-of-the-art baselines.