Inspired by the Weisfeiler--Lehman graph kernel, we augment its iterative feature map construction approach by a set of multi-scale topological features. More precisely, we leverage propagated node label information to transform an unweighted graph into a metric one. We then use persistent homology, a technique from topological data analysis, to assess the topological properties, i.e. connected components and cycles, of the metric graph. Through this process, each graph can be represented similarly to the original Weisfeiler--Lehman sub-tree feature map.
We demonstrate the utility and improved accuracy of our method on numerous graph data sets while also discussing theoretical aspects of our approach.
Bastian Rieck (ETH Zurich)
Christian Bock (ETH Zurich)
Karsten Borgwardt (ETH Zurich)
Karsten Borgwardt is Professor of Data Mining at ETH Zürich, at the Department of Biosystems located in Basel. His work has won several awards, including the NIPS 2009 Outstanding Paper Award, the Krupp Award for Young Professors 2013 and a Starting Grant 2014 from the ERC-backup scheme of the Swiss National Science Foundation. Since 2013, he is heading the Marie Curie Initial Training Network for "Machine Learning for Personalized Medicine" with 12 partner labs in 8 countries (http://www.mlpm.eu). The business magazine "Capital" listed him as one of the "Top 40 under 40" in Science in/from Germany in 2014, 2015 and 2016. For more information, visit: https://www.bsse.ethz.ch/mlcb
Related Events (a corresponding poster, oral, or spotlight)
2019 Poster: A Persistent Weisfeiler--Lehman Procedure for Graph Classification »
Tue Jun 11th 06:30 -- 09:00 PM Room Pacific Ballroom