{"type":"ScholarlyArticle","title":"Kernel Readout for Graph Neural Networks","authors":[{"name":"Jiajun Yu","slug":"jiajun-yu","given":"Jiajun","family":"Yu","affiliation":"College of Information and Electrical Engineering , China Agricultural University , Beijing , China; Shenzhen Research Institute of Big Data , Shenzhen , China; College of Computer Science and Technology , Zhejiang University , Hangzhou , China"},{"name":"Zhihao Wu","slug":"zhihao-wu","given":"Zhihao","family":"Wu","affiliation":"School of Data Science , The Chinese University of Hong Kong , Shenzhen (CUHK-Shenzhen) , China; Shenzhen Research Institute of Big Data , Shenzhen , China"},{"name":"Jinyu Cai","slug":"jinyu-cai","given":"Jinyu","family":"Cai","affiliation":"Institute of Data Science , National University of Singapore , Singapore"},{"name":"Adele Lu Jia","slug":"adele-lu-jia","given":"Adele Lu","family":"Jia","affiliation":"College of Information and Electrical Engineering , China Agricultural University , Beijing , China"},{"name":"Jicong Fan","slug":"jicong-fan","given":"Jicong","family":"Fan","affiliation":"School of Data Science , The Chinese University of Hong Kong , Shenzhen (CUHK-Shenzhen) , China; Shenzhen Research Institute of Big Data , Shenzhen , China"}],"abstract":"Graph neural networks (GNNs) for graph classification or representation learning require a pooling operation to convert the nodes' embeddings of each graph to a vector as the graph-level representation and the operation has a significant impact on model accuracy. The paper presents a novel graph pooling method called Kernel Readout (KerRead). KerRead maps the node embeddings from the sample space with limited nodes to an augmented sample space with infinite nodes, and then calculates the inner product between some learnable adaptive centers and the augmented node embeddings, which forms a final graph-level feature vector. We apply the proposed strategy to six supervised and two unsupervised graph neural networks such as GCN, GIN, GUNet, InfoGraph, and GraphCL, and the experiments on eight benchmark datasets show that the proposed readout outperforms classical pooling methods such as Sum and seven state-of-the-art pooling methods such as SRead and Janossy GRU. Code and Appendix are both available at https://github.com/jiajunCAU/KerRead.","doi":"10.24963/ijcai.2024/277","url":"https://ijcai.org/proceedings/2024/277","pdf":"https://ijcai.org/proceedings/2024/0277.pdf","markdown":"https://ijcai.org/proceedings/2024/277.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/277","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":"Data Mining","track":"Main Track","number":277,"pages":{"first":2505,"last":2514},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Representation learning"],"videos":[],"references":[{"position":1,"raw":"[Bruna et al., 2014] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 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