2 papers
cs.LG2026
CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning
PinHeng Zong, Ye Yuan
Dynamic graphs are widely used to model time-evolving relational systems in real-world applications. Dynamic graph neural networks provide an effective framework for capturing both…
cs.LG2025
FGC-Comp: Adaptive Neighbor-Grouped Attribute Completion for Graph-based Anomaly Detection
Junpeng Wu, Pinheng Zong
Graph-based Anomaly Detection models have gained widespread adoption in recent years, identifying suspicious nodes by aggregating neighborhood information. However, most existing s…