AI RESEARCH
Graph Retention Networks for Dynamic Graphs
arXiv CS.LG
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ArXi:2411.11259v3 Announce Type: replace 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 data as graph retention, equipping the model with three key computational paradigms: parallelizable