融合图神经网络与拓扑特征，提升图分类性能
Cross-View Topology-Aware Graph Representation Learning
- 双视角对比学习：结合GNN结构嵌入与持久同调拓扑嵌入
- 在TU和OGB分子数据集上超越现有最优模型
- 适合需要强拓扑感知的图分类任务
图分类在化学、社交网络和生物信息学中受到广泛关注。尽管图神经网络（GNN）能有效捕捉局部结构模式，但常忽略对鲁棒表示学习至关重要的全局拓扑特征。本文提出GraphTCL，一种双视角对比学习框架，将GNN的结构嵌入与持久同调推导出的拓扑嵌入相结合，通过跨视角对比损失对齐互补视图，从而提升表示质量并改善分类性能。在包括TU和OGB分子图在内的基准数据集上的大量实验表明，GraphTCL始终优于当前最先进的基线方法。该研究凸显了拓扑感知对比学习在推进图表示学习中的重要性。
原文摘要 · Abstract (English)
Graph classification has gained significant attention due to its applications in chemistry, social networks, and bioinformatics. While Graph Neural Networks (GNNs) effectively capture local structural patterns, they often overlook global topological features that are critical for robust representation learning. In this work, we propose GraphTCL, a dual-view contrastive learning framework that integrates structural embeddings from GNNs with topological embeddings derived from persistent homology. By aligning these complementary views through a cross-view contrastive loss, our method enhances representation quality and improves classification performance. Extensive experiments on benchmark datasets, including TU and OGB molecular graphs, demonstrate that GraphTCL consistently outperforms state-of-the-art baselines. This study highlights the importance of topology-aware contrastive learning for advancing graph representation methods.
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