{"type":"ScholarlyArticle","title":"HA-SCN: Learning Hierarchical Aligned Subtree Convolutional Networks 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":"Hangyuan Du","slug":"hangyuan-du","given":"Hangyuan","family":"Du","affiliation":""},{"name":"Yue Wang","slug":"yue-wang","given":"Yue","family":"Wang","affiliation":""},{"name":"Edwin Hancock","slug":"edwin-hancock","given":"Edwin","family":"Hancock","affiliation":""}],"abstract":"In this paper, we propose a Hierarchical Aligned Subtree Convolutional Network (HA-SCN) for graph classification. Our idea is to transform graphs of arbitrary sizes into fixed-sized aligned graphs and construct a normalized K-layer m-ary subtree for each node in the aligned graphs. By sliding convolutional filters over the entire subtree at each node, we define a novel subtree convolution and pooling operation that hierarchically abstracts node-level information. We demonstrate that the proposed HA-SCN model not only realizes the convolution mechanism similar to the Convolutional Neural Networks (CNNs), which have the characteristics of weight sharing and fixed-sized receptive fields, but also effectively mitigates the over-squashing problem. Meanwhile, it establishes the correspondence information between nodes, alleviating the information loss issue. Experimental results on various benchmark graph datasets show that our approach achieves state-of-the-art performance in graph classification tasks.","doi":"10.24963/ijcai.2025/361","url":"https://ijcai.org/proceedings/2025/361","pdf":"https://ijcai.org/proceedings/2025/0361.pdf","markdown":"https://ijcai.org/proceedings/2025/361.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/361","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":361,"pages":{"first":3245,"last":3253},"keywords":["Machine Learning: ML: Convolutional networks","Data Mining: DM: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}