Go to ACM TheWebConf 2025 Conference homepageGraph Self-Supervised Learning with Learnable Structural and Positional Encodings
We propose a novel framework that addresses a critical limitation in Graph Self-Supervised Learning (GSSL) for graph classification: the underestimation of topological information. Traditional GSSL, despite its success in various benchmarks, often fails to fully leverage the expressive power of Graph Neural Networks (GNNs), particularly in capturing complex structural properties. This limitation stems from two main factors: (1) the inadequacy of conventional GNNs in representing sophisticated topological features, and (2) the focus of self-supervised learning solely on final graph representations. To address these issues, we introduce GenHopNet, a GNN framework that integrates a k-hop message-passing scheme, enhancing its ability to capture local structural information without explicit substructure extraction. We theoretically demonstrate that GenHopNet surpasses the expressiveness of the classical Weisfeiler-Lehman (WL) test for graph isomorphism. Furthermore, we propose a structural- and positional-aware GSSL framework that incorporates topological information throughout the learning process. This approach enables the learning of representations that are both sensitive to graph topology and invariant to specific structural and feature augmentations. Comprehensive experiments on graph classification datasets, including those designed to test structural sensitivity, show that our methods consistently outperform most of the existing approaches in accuracy while maintaining computational efficiency. Our work significantly advances GSSL's capability in distinguishing graphs with similar local structures but different global topologies.