5 papers
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…
TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
Wen Shi, Zhe Wang, Huafei Huang +6
Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the majority. While existing text-ri…
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…
GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection
Saba Fathi Rabooki, Bowen Li, Falih Gozi Febrinanto +4
Cyber-physical-social systems (CPSSs) have emerged in many applications over recent decades, requiring increased attention to security concerns. The rise of sophisticated threats l…
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…