An Empirical Evaluation of Temporal Graph Benchmark

TL;DR

This paper empirically evaluates the Temporal Graph Benchmark by extending DyGLib, comparing multiple dynamic graph learning methods, and providing improved baseline results to facilitate future research in dynamic graph analysis.

Contribution

It introduces an extended evaluation framework for TGB with eleven models and demonstrates improved baseline performances, aiding researchers in dynamic graph learning.

Findings

Models show varying performance across datasets.

Baseline performances can be significantly improved.

Resources are publicly available for community use.

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.