{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-persistent-weisfeilerlehman-procedure-for","title":"A Persistent Weisfeiler–Lehman Procedure for Graph Classification","arxiv_id":null,"date":"2019-06-09","proceeding":"Proceedings of the 36th International Conference on Machine Learning 2019 6","authors":["Bastian Rieck","Christian Bock","Karsten Borgwardt"],"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.","url_abs":"http://proceedings.mlr.press/v97/rieck19a.html","url_pdf":"http://proceedings.mlr.press/v97/rieck19a/rieck19a.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-persistent-weisfeilerlehman-procedure-for","repo_url":"https://github.com/BorgwardtLab/P-WL","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"topological-data-analysis","task_name":"Topological Data Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-classification-on-mutag","task":"Graph Classification","dataset":"MUTAG","model":"P-WL-C","rank_in_archive_order":73,"of":74,"metrics":{"Mean Accuracy":"90.51"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-proteins","task":"Graph Classification","dataset":"PROTEINS","model":"P-WL-UC","rank_in_archive_order":67,"of":103,"metrics":{"Accuracy":"75.36%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-property-prediction-on-ogbg-molhiv","task":"Graph Property Prediction","dataset":"ogbg-molhiv","model":"P-WL","rank_in_archive_order":19,"of":43,"metrics":{"Ext. data":"No","Number of params":"4600000","Test ROC-AUC":"0.8039 ± 0.0040","Validation ROC-AUC":"0.8279 ± 0.0059"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}