DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs

arXiv:2602.23135v2 Announce Type: replace Abstract: Edge classification on directed dynamic graphs requires modeling interactions between source and destination nodes exhibiting asymmetrical behavioral patterns and temporal dynamics. However, existing dynamic graph architectures largely rely on shared parameters for processing source and destination nodes, with limited or no systematic role-aware modeling. We propose DyGnROLE (Dynamic Graph Node-Role-Oriented Latent Encoding), a Transformer-based architecture that disentangles source and destination representations. By using separate embedding

Source: arXiv cs.LG — read the full report at the original publisher.

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