  We propose DyGFormer, a new Transformer-based architecture for dynamic graph
learning. DyGFormer is conceptually simple and only needs to learn from nodes'
historical first-hop interactions by: (1) a neighbor co-occurrence encoding
scheme that explores the correlations of the source node and destination node
based on their historical sequences; (2) a patching technique that divides each
sequence into multiple patches and feeds them to Transformer, allowing the
model to effectively and efficiently benefit from longer histories. We also
introduce DyGLib, a unified library with standard training pipelines,
extensible coding interfaces, and comprehensive evaluating protocols to promote
reproducible, scalable, and credible dynamic graph learning research. By
performing exhaustive experiments on thirteen datasets for dynamic link
prediction and dynamic node classification tasks, we find that DyGFormer
achieves state-of-the-art performance on most of the datasets, demonstrating
its effectiveness in capturing nodes' correlations and long-term temporal
dependencies. Moreover, some results of baselines are inconsistent with
previous reports, which may be caused by their diverse but less rigorous
implementations, showing the importance of DyGLib. All the used resources are
publicly available at https://github.com/yule-BUAA/DyGLib.
