{"type":"ScholarlyArticle","title":"A Simple yet Effective Method for Graph Classification","authors":[{"name":"Junran Wu","slug":"junran-wu","given":"Junran","family":"Wu","affiliation":"State Key Lab of Software Development Environment , Beihang University , Beijing , 100191 , China"},{"name":"Shangzhe Li","slug":"shangzhe-li","given":"Shangzhe","family":"Li","affiliation":"State Key Lab of Software Development Environment , Beihang University , Beijing , 100191 , China; School of Mathematical Science , Beihang University , Beijing 100191 , China"},{"name":"Jianhao Li","slug":"jianhao-li","given":"Jianhao","family":"Li","affiliation":"State Key Lab of Software Development Environment , Beihang University , Beijing , 100191 , China"},{"name":"Yicheng Pan","slug":"yicheng-pan","given":"Yicheng","family":"Pan","affiliation":"State Key Lab of Software Development Environment , Beihang University , Beijing , 100191 , China"},{"name":"Ke Xu","slug":"ke-xu","given":"Ke","family":"Xu","affiliation":"State Key Lab of Software Development Environment , Beihang University , Beijing , 100191 , China"}],"abstract":"In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the complexity of such models. Intuitively, given a problem, a simpler data structure comes with a simpler algorithm. Here, we investigate the feasibility of improving graph classification performance while simplifying the learning process. Inspired by structural entropy on graphs, we transform the data sample from graphs to coding trees, which is a simpler but essential structure for graph data. Furthermore, we propose a novel message passing scheme, termed hierarchical reporting, in which features are transferred from leaf nodes to root nodes by following the hierarchical structure of coding trees. We then present a tree kernel and a convolutional network to implement our scheme for graph classification. With the designed message passing scheme, the tree kernel and convolutional network have a lower runtime complexity of O(n) than Weisfeiler-Lehman subtree kernel and other graph neural networks of at least O(hm). We empirically validate our methods with several graph classification benchmarks and demonstrate that they achieve better performance and lower computational consumption than competing approaches.","doi":"10.24963/ijcai.2022/497","url":"https://ijcai.org/proceedings/2022/497","pdf":"https://ijcai.org/proceedings/2022/0497.pdf","markdown":"https://ijcai.org/proceedings/2022/497.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/497","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":497,"pages":{"first":3580,"last":3586},"keywords":["Machine Learning: Sequence and Graph Learning","Machine Learning: Representation learning","Machine Learning: Classification","Machine Learning: Optimisation"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986982","length_sec":85,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985125","length_sec":707,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Atwood and Towsley, 2016] James Atwood and Don Towsley. 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