[R] Yale & IBM Propose KerGNNs: Interpretable GNNs with Graph Kernels That Achieve SOTA-Competitive Performance

A research team from Yale and IBM presents Kernel Graph Neural Networks (KerGNNs), which integrate graph kernels into the message passing process of GNNs in one framework, achieving performance comparable to state-of-the-art methods and significantly improving model interpretability compared with conventional GNNs.

Here is a quick read: Yale & IBM Propose KerGNNs: Interpretable GNNs with Graph Kernels That Achieve SOTA-Competitive Performance.

The paper KerGNNs: Interpretable Graph Neural Networks with Graph Kernels is on arXiv.

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