Title: GET: Rethinking Dynamic Graph Learning as Global Event Sequence Generation
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph Learning, Temporal Graph, Transformer, Link Prediction, Dynamic Graph

Abstract: The prevailing paradigms in dynamic graph learning, which rely on local neighbor propagation or node-specific memory updates, are fundamentally limited in their ability to model global interaction patterns and scale inference efficiently. Rethinking these challenges, 
we propose GET (Global Event Transformer), a generative framework that reformulates dynamic graph learning as global event sequence generation. By processing the entire interaction history as a unified sequence, GET's architecture is designed to capture dependencies beyond the local receptive fields of prior methods, while its generative nature eliminates the candidate-wise inference bottleneck of discriminative models. GET flexibly incorporates structural priors through lightweight GNN encoders and memory modules to enhance its adaptability to diverse graph structures. Extensive experiments on five large-scale TGB benchmarks and six additional datasets show that GET achieves strong competitive performance on dynamic link prediction while delivering substantially faster inference (21.4 × on tgbl-wiki) compared to strong baselines. Our work presents a practical and effective framework that balances expressive global modeling with scalable inference, offering a robust solution for large-scale dynamic graphs.

TL;DR: A generative Transformer-based model for dynamic graph learning with structural-temporal context and efficient sequence prediction.