{"type":"ScholarlyArticle","title":"Deep Hierarchical Graph Alignment Kernels","authors":[{"name":"Shuhao Tang","slug":"shuhao-tang","given":"Shuhao","family":"Tang","affiliation":"Tongji University , Shanghai , China"},{"name":"Hao Tian","slug":"hao-tian","given":"Hao","family":"Tian","affiliation":"Tongji University , Shanghai , China"},{"name":"Xiaofeng Cao","slug":"xiaofeng-cao","given":"Xiaofeng","family":"Cao","affiliation":"Jilin University , Changchun , China"},{"name":"Wei Ye","slug":"wei-ye","given":"Wei","family":"Ye","affiliation":"Tongji University , Shanghai , China"}],"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 the Reproducing Kernel Hilbert Space (RKHS), where graph feature maps are derived by kernel mean embedding. Theoretical analysis guarantees that DHGAK is positive semi-definite and has linear separability in the RKHS. Comparison with state-of-the-art graph kernels on various benchmark datasets demonstrates the effectiveness and efficiency of DHGAK. The code is available at Github (https://github.com/EWesternRa/DHGAK).","doi":"10.24963/ijcai.2024/549","url":"https://ijcai.org/proceedings/2024/549","pdf":"https://ijcai.org/proceedings/2024/0549.pdf","markdown":"https://ijcai.org/proceedings/2024/549.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/549","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":549,"pages":{"first":4964,"last":4972},"keywords":["Machine Learning: ML: Kernel methods"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2022] Lu Bai, Lixin Cui, and Hancock Edwin. 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