{"type":"ScholarlyArticle","title":"Multi-View Robust Graph Representation Learning for Graph Classification","authors":[{"name":"Guanghui Ma","slug":"guanghui-ma","given":"Guanghui","family":"Ma","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China"},{"name":"Chunming Hu","slug":"chunming-hu","given":"Chunming","family":"Hu","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China; College of Software , Beihang University , Beijing , China; Zhongguancun Laboratory , Beijing , China"},{"name":"Ling Ge","slug":"ling-ge","given":"Ling","family":"Ge","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China"},{"name":"Hong Zhang","slug":"hong-zhang","given":"Hong","family":"Zhang","affiliation":"National Computer Network Emergency Response Technical Team / Coordination Center of China"}],"abstract":"The robustness of graph classification models plays an essential role in providing highly reliable applications. Previous studies along this line primarily focus on seeking the stability of the model in terms of  overall data metrics (e.g., accuracy)  when facing data perturbations, such as removing edges. Empirically, we find that these graph classification models also suffer from semantic bias and confidence collapse issues, which substantially hinder their applicability in real-world scenarios. To address these issues, we present  MGRL, a multi-view representation learning model for graph classification tasks that achieves robust results. Firstly, we proposes an instance-view consistency representation learning method, which utilizes multi-granularity contrastive learning technique to perform semantic constraints on instance representations at both the node and graph levels, thus alleviating the semantic bias issue. Secondly, we proposes a class-view discriminative representation learning method, which employs the prototype-driven class distance optimization technique to adjust intra- and inter-class distances, thereby mitigating the confidence collapse issue.Finally, extensive experiments and visualizations on eight benchmark dataset demonstrate the effectiveness of MGRL.","doi":"10.24963/ijcai.2023/449","url":"https://ijcai.org/proceedings/2023/449","pdf":"https://ijcai.org/proceedings/2023/0449.pdf","markdown":"https://ijcai.org/proceedings/2023/449.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/449","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":449,"pages":{"first":4037,"last":4045},"keywords":["Machine Learning: ML: Representation learning","Machine Learning: ML: Robustness"],"videos":[],"references":[{"position":1,"raw":"[Chu et al., 2021] Guanyi Chu, Xiao Wang, Chuan Shi, and Xunqiang Jiang. 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