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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★ 1 cited
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…