ML Anthology

Benchmarking GNNs with GenCAT Workbench

Seiji Maekawa, Yuya Sasaki, George Fletcher, Makoto Onizuka
ECML-PKDD 2022 pp. 607-611
doi:10.1007/978-3-031-26422-1_40 /ecmlpkdd/2022/maekawa2022ecmlpkdd-benchmarking/

Abstract

We present GenCAT Workbench, an end-to-end framework with which users can generate synthetic attributed graphs with node labels and evaluate their graph analytic methods, e.g., graph neural networks (GNNs), on the generated graphs. GenCAT Workbench supports various types of graphs with controlled node attributes and graph topology. We demonstrate the GenCAT Workbench and how it clarifies the strong and weak points of GNN models. Our code base is available on Github ( https://github.com/seijimaekawa/GenCAT/tree/main/GenCAT_Workbench ).

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Cite

Text

Maekawa et al. "Benchmarking GNNs with GenCAT Workbench." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26422-1_40

Markdown

[Maekawa et al. "Benchmarking GNNs with GenCAT Workbench." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/maekawa2022ecmlpkdd-benchmarking/) doi:10.1007/978-3-031-26422-1_40

BibTeX

@inproceedings{maekawa2022ecmlpkdd-benchmarking,
  title     = {{Benchmarking GNNs with GenCAT Workbench}},
  author    = {Maekawa, Seiji and Sasaki, Yuya and Fletcher, George and Onizuka, Makoto},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {607-611},
  doi       = {10.1007/978-3-031-26422-1_40},
  url       = {https://mlanthology.org/ecmlpkdd/2022/maekawa2022ecmlpkdd-benchmarking/}
}