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cs.LG2026
Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
Qian Chang, Ciprian Doru Giurcaneanu, Runsong Jia +6
Representation learning on dynamic graphs requires capturing complex dependencies that evolve across both time and structure. Existing approaches typically adopt fixed temporal dec…
cs.LG2024
Graph Retention Networks for Dynamic Graphs
Qian Chang, Xia Li, Xiufeng Cheng +4
In this paper, we propose Graph Retention Networks (GRNs) as a unified architecture for deep learning on dynamic graphs. The GRN extends the concept of retention into dynamic graph…