{"type":"ScholarlyArticle","title":"DHTAGK: Deep Hierarchical Transitive-Aligned Graph Kernels for Graph Classification","authors":[{"name":"Xinya Qin","slug":"xinya-qin","given":"Xinya","family":"Qin","affiliation":""},{"name":"Lu Bai","slug":"lu-bai","given":"Lu","family":"Bai","affiliation":""},{"name":"Lixin Cui","slug":"lixin-cui","given":"Lixin","family":"Cui","affiliation":""},{"name":"Ming Li","slug":"ming-li","given":"Ming","family":"Li","affiliation":""},{"name":"Ziyu Lyu","slug":"ziyu-lyu","given":"Ziyu","family":"Lyu","affiliation":""},{"name":"Hangyuan Du","slug":"hangyuan-du","given":"Hangyuan","family":"Du","affiliation":""},{"name":"Edwin Hancock","slug":"edwin-hancock","given":"Edwin","family":"Hancock","affiliation":""}],"abstract":"In this paper, we propose a family of novel Deep Hierarchical Transitive-Aligned Graph Kernels (DHTAGK) for graph classification. To this end, we commence by developing a new Hierarchical Aligned Graph Auto-Encoder (HA-GAE) to construct transitive-aligned embedding graphs that encapsulate the structural correspondence information between graphs. The DHTAGK kernels then measure either the Jensen-Shannon Divergence between the adjacency matrices or the Gaussian kernel between the node feature matrices of the embedding graphs. Unlike the classical R-convolution kernels and node-based alignment kernels, the DHTAGK kernels can capture the transitive structural correspondence information and thus ensure the positive definiteness. Furthermore, the HA-GAE enables the DHTAGK kernels to simultaneously reflect both local and global graph structures and identify common structural patterns. Experimental results show that the DHTAGK kernels outperform state-of-the-art graph kernels and deep learning methods on benchmark datasets.","doi":"10.24963/ijcai.2025/362","url":"https://ijcai.org/proceedings/2025/362","pdf":"https://ijcai.org/proceedings/2025/0362.pdf","markdown":"https://ijcai.org/proceedings/2025/362.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/362","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":362,"pages":{"first":3254,"last":3262},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Kernel methods"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}