Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

NeurIPS 2019 Simon S. DuKangcheng HouBarnabás PóczosRuslan SalakhutdinovRuosong WangKeyulu Xu

While graph kernels (GKs) are easy to train and enjoy provable theoretical guarantees, their practical performances are limited by their expressive power, as the kernel function often depends on hand-crafted combinatorial features of graphs. Compared to graph kernels, graph neural networks (GNNs) usually achieve better practical performance, as GNNs use multi-layer architectures and non-linear activation functions to extract high-order information of graphs as features... (read more)

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