A Novel Higher-order Weisfeiler-Lehman Graph Convolution
C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR, Bangkok, Thailand, 2020, pp. 49–64.
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Editor
Jialin Pan, Sinno;
Sugiyama, Masashi
Department
Abstract
Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs.
Publishing Year
Proceedings Title
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
forms.conference.field.series_title_volume.label
Proceedings of Machine Learning Research
Volume
129
Page
49-64
Conference
Asian Conference on Machine Learning
Conference Location
Bangkok, Thailand
Conference Date
2020-11-18 – 2020-11-20
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Cite this
Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.
Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.
@inproceedings{Damke_Melnikov_Hüllermeier_2020, place={Bangkok, Thailand}, series={Proceedings of Machine Learning Research}, title={A Novel Higher-order Weisfeiler-Lehman Graph Convolution}, volume={129}, booktitle={Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)}, publisher={PMLR}, author={Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Jialin Pan, Sinno and Sugiyama, MasashiEditors}, year={2020}, pages={49–64}, collection={Proceedings of Machine Learning Research} }
Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020.
C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman Graph Convolution,” in Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 2020, vol. 129, pp. 49–64.
Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64.
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