{"type":"ScholarlyArticle","title":"GraphDIVE: Graph Classification by Mixture of Diverse Experts","authors":[{"name":"Fenyu Hu","slug":"fenyu-hu","given":"Fenyu","family":"Hu","affiliation":"Center for Research on Intelligent Perception and Computing , National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Liping Wang","slug":"liping-wang","given":"Liping","family":"Wang","affiliation":"Center for Research on Intelligent Perception and Computing , National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Qiang Liu","slug":"qiang-liu","given":"Qiang","family":"Liu","affiliation":"Center for Research on Intelligent Perception and Computing , National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Shu Wu","slug":"shu-wu","given":"Shu","family":"Wu","affiliation":"Center for Research on Intelligent Perception and Computing , National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Liang Wang","slug":"liang-wang","given":"Liang","family":"Wang","affiliation":"Center for Research on Intelligent Perception and Computing , National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Tieniu Tan","slug":"tieniu-tan","given":"Tieniu","family":"Tan","affiliation":"Center for Research on Intelligent Perception and Computing , National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"}],"abstract":"Graph classification is a challenging research task in many applications across a broad range of domains. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world graph datasets. Despite their successes, most of current GNN models largely suffer from the ubiquitous class imbalance problem, which typically results in prediction bias towards majority classes. Although many imbalanced learning methods have been proposed, they mainly focus on regular Euclidean data and cannot well utilize topological structure of graph (non-Euclidean) data.  To boost the performance of GNNs and investigate the relationship between topological structure and class imbalance, we propose GraphDIVE, which learns multi-view graph representations and combine multi-view experts (i.e., classifiers). Specifically, multi-view graph representations correspond to the intrinsic diverse graph topological structure characteristics. Extensive experiments on molecular benchmark datasets demonstrate the effectiveness of the proposed approach.","doi":"10.24963/ijcai.2022/289","url":"https://ijcai.org/proceedings/2022/289","pdf":"https://ijcai.org/proceedings/2022/0289.pdf","markdown":"https://ijcai.org/proceedings/2022/289.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/289","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":"Data Mining","track":"Main Track","number":289,"pages":{"first":2080,"last":2086},"keywords":["Machine Learning: Sequence and Graph Learning","Data Mining: Mining Graphs"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986862","length_sec":86,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985005","length_sec":334,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Blum and Mitchell, 1998] Avrim Blum and Tom Mitchell. 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