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cs.LG2026
ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
Hongjiang Chen, Xin Zheng, Pengfei Jiao +5
Temporal graph neural networks (TGNNs) have gained significant traction for solving real-world temporal graph tasks. However, their interpretability remains limited, as most TGNNs…
cs.LG2026
Spiking Graph Predictive Coding for Reliable OOD Generalization
Jing Ren, Jiapeng Du, Bowen Li +6
Graphs provide a powerful basis for modeling Web-based relational data, with expressive GNNs to support the effective learning in dynamic web environments. However, real-world depl…
cs.LG2025★ 1 cited
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning
Shuo Yu, Yingbo Wang, Ruolin Li +7
Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve…