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Deep Hierarchical Graph Alignment Kernels
May 10, 2024, 4:41 a.m. | Shuhao Tang, Hao Tian, Xiaofeng Cao, Wei Ye
cs.LG updates on arXiv.org arxiv.org
Abstract: Typical R-convolution graph kernels invoke the kernel functions that decompose graphs into non-isomorphic substructures and compare them. However, overlooking implicit similarities and topological position information between those substructures limits their performances. In this paper, we introduce Deep Hierarchical Graph Alignment Kernels (DHGAK) to resolve this problem. Specifically, the relational substructures are hierarchically aligned to cluster distributions in their deep embedding space. The substructures belonging to the same cluster are assigned the same feature map in …
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