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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Notes
- 1.
Our demo video is available on https://www.youtube.com/watch?v=28xVOHRDpCE.
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Acknowledgement
This work was supported by JSPS KAKENHI Grant Numbers JP20H00583 and JST PRESTO Grant Number JPMJPR21C5.
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Maekawa, S., Sasaki, Y., Fletcher, G., Onizuka, M. (2023). Benchmarking GNNs with GenCAT Workbench. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13718. Springer, Cham. https://doi.org/10.1007/978-3-031-26422-1_40
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DOI: https://doi.org/10.1007/978-3-031-26422-1_40
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