Papers › Propagation kernels: efficient graph kernels from propagated information
Propagation kernels: efficient graph kernels from propagated information
Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | D&D | Propagation kernels (pk) | Accuracy | 78.8% | #20 of 53 | Archive leaderboard | report |
| Graph Classification | MUTAG | Propagation kernels (pk) | Accuracy | 84.5% | #63 of 74 | Archive leaderboard | report |
| Graph Classification | NCI1 | Propagation kernels (pk) | Accuracy | 84.5% | #19 of 69 | Archive leaderboard | report |
| Graph Classification | NCI109 | Propagation kernels (pk) | Accuracy | 83.5 | #11 of 38 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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