Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited

Publications: Contribution to conferencePaperPeer Reviewed

Abstract

Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism. We give a unified view on both classes of graph kernels. We study walk-based node refinement methods and formally relate them to several widely-used techniques, including Morgan's algorithm for molecule canonization and the Weisfeiler-Leman test. We define corresponding walk-based kernels on nodes that allow fine-grained parameterized neighborhood comparison, reach Weisfeiler-Leman expressiveness, and are computed using the kernel trick. From this we show that classical random walk kernels with only minor modifications regarding definition and computation are as expressive as the widely-used Weisfeiler-Leman subtree kernel but support non-strict neighborhood comparison. We verify experimentally that walk-based kernels reach or even surpass the accuracy of Weisfeiler-Leman kernels in real-world classification tasks.
Original languageEnglish
DOIs
Publication statusPublished - 28 Nov 2022
EventThirty-sixth Conference on Neural Information Processing Systems: Neurips 2022 - New Orleans Convention Center (hybrid), New Orleans, United States
Duration: 28 Nov 20229 Dec 2022
https://nips.cc/

Conference

ConferenceThirty-sixth Conference on Neural Information Processing Systems
Country/TerritoryUnited States
CityNew Orleans
Period28/11/229/12/22
Internet address

Austrian Fields of Science 2012

  • 102019 Machine learning

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