CRAFT replaces memory and aggregation with learnable node embeddings and destination-to-source-neighbor cross-attention, improving future link prediction on most of 17 temporal graph benchmarks.
An Empirical Evaluation of Temporal Graph Benchmark
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abstract
In this paper, we conduct an empirical evaluation of Temporal Graph Benchmark (TGB) by extending our Dynamic Graph Library (DyGLib) to TGB. Compared with TGB, we include eleven popular dynamic graph learning methods for more exhaustive comparisons. Through the experiments, we find that (1) different models depict varying performance across various datasets, which is in line with previous observations; (2) the performance of some baselines can be significantly improved over the reported results in TGB when using DyGLib. This work aims to ease the researchers' efforts in evaluating various dynamic graph learning methods on TGB and attempts to offer results that can be directly referenced in the follow-up research. All the used resources in this project are publicly available at https://github.com/yule-BUAA/DyGLib_TGB. This work is in progress, and feedback from the community is welcomed for improvements.
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Future Link Prediction Without Memory or Aggregation
CRAFT replaces memory and aggregation with learnable node embeddings and destination-to-source-neighbor cross-attention, improving future link prediction on most of 17 temporal graph benchmarks.