A Persistent Weisfeiler-Lehman Procedure for Graph Classification

Bastian Rieck, Christian Bock, Karsten Borgwardt
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5448-5458, 2019.

Abstract

The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks. However, its subtree features are not able to capture connected components and cycles, topological features known for characterising graphs. To extract such features, we leverage propagated node label information and transform unweighted graphs into metric ones. This permits us to augment the subtree features with topological information obtained using persistent homology, a concept from topological data analysis. Our method, which we formalise as a generalisation of Weisfeiler–Lehman subtree features, exhibits favourable classification accuracy and its improvements in predictive performance are mainly driven by including cycle information.

Cite this Paper


BibTeX
@InProceedings{pmlr-v97-rieck19a, title = {A Persistent Weisfeiler-Lehman Procedure for Graph Classification}, author = {Rieck, Bastian and Bock, Christian and Borgwardt, Karsten}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {5448--5458}, year = {2019}, editor = {Kamalika Chaudhuri and Ruslan Salakhutdinov}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/rieck19a/rieck19a.pdf}, url = { http://proceedings.mlr.press/v97/rieck19a.html }, abstract = {The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks. However, its subtree features are not able to capture connected components and cycles, topological features known for characterising graphs. To extract such features, we leverage propagated node label information and transform unweighted graphs into metric ones. This permits us to augment the subtree features with topological information obtained using persistent homology, a concept from topological data analysis. Our method, which we formalise as a generalisation of Weisfeiler–Lehman subtree features, exhibits favourable classification accuracy and its improvements in predictive performance are mainly driven by including cycle information.} }
Endnote
%0 Conference Paper %T A Persistent Weisfeiler-Lehman Procedure for Graph Classification %A Bastian Rieck %A Christian Bock %A Karsten Borgwardt %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-rieck19a %I PMLR %P 5448--5458 %U http://proceedings.mlr.press/v97/rieck19a.html %V 97 %X The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks. However, its subtree features are not able to capture connected components and cycles, topological features known for characterising graphs. To extract such features, we leverage propagated node label information and transform unweighted graphs into metric ones. This permits us to augment the subtree features with topological information obtained using persistent homology, a concept from topological data analysis. Our method, which we formalise as a generalisation of Weisfeiler–Lehman subtree features, exhibits favourable classification accuracy and its improvements in predictive performance are mainly driven by including cycle information.
APA
Rieck, B., Bock, C. & Borgwardt, K.. (2019). A Persistent Weisfeiler-Lehman Procedure for Graph Classification. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:5448-5458 Available from http://proceedings.mlr.press/v97/rieck19a.html .

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