BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
Abstract
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGNNs, the previous TGNN evaluations reveal several limitations regarding four critical issues: 1) inconsistent datasets, 2) inconsistent evaluation pipelines, 3) lacking workload diversity, and 4) lacking efficient comparison. Overall, there lacks an empirical study that puts TGNN models onto the same ground and compares them comprehensively. To this end, we propose BenchTemp, a general benchmark for evaluating TGNN models on various workloads. BenchTemp provides a set of benchmark datasets so that different TGNN models can be fairly compared. Further, BenchTemp engineers a standard pipeline that unifies the TGNN evaluation. With BenchTemp, we extensively compare the representative TGNN models on different tasks (e.g., link prediction and node classification) and settings (transductive and inductive), w.r.t. both effectiveness and efficiency metrics. We have made BenchTemp publicly available at https://github.com/qianghuangwhu/benchtemp.
Cite
@article{arxiv.2308.16385,
title = {BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks},
author = {Qiang Huang and Jiawei Jiang and Xi Susie Rao and Ce Zhang and Zhichao Han and Zitao Zhang and Xin Wang and Yongjun He and Quanqing Xu and Yang Zhao and Chuang Hu and Shuo Shang and Bo Du},
journal= {arXiv preprint arXiv:2308.16385},
year = {2023}
}
Comments
28 pages, 23 figures, 27 tables. Submitted to the Conference on Neural Information Processing Systems 2023 Track on Datasets and Benchmarks