中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
GraphDIVE: Graph Classification by Mixture of Diverse Experts

文献类型:会议论文

作者Hu,Fenyu; Wang,Liping; Liu,Qiang; Wu,Shu; Wang,Liang; Tan,Tieniu
出版日期2022
会议日期July 23-29, 2022
会议地点Messe Wien, Vienna, Austria.
英文摘要

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.

源URL[http://ir.ia.ac.cn/handle/173211/52179]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Wu,Shu
作者单位Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Hu,Fenyu,Wang,Liping,Liu,Qiang,et al. GraphDIVE: Graph Classification by Mixture of Diverse Experts[C]. 见:. Messe Wien, Vienna, Austria.. July 23-29, 2022.

入库方式: OAI收割

来源:自动化研究所

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