{"type":"ScholarlyArticle","title":"Information Augmentation for Few-shot Node Classification","authors":[{"name":"Zongqian Wu","slug":"zongqian-wu","given":"Zongqian","family":"Wu","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Peng Zhou","slug":"peng-zhou","given":"Peng","family":"Zhou","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Guoqiu Wen","slug":"guoqiu-wen","given":"Guoqiu","family":"Wen","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Yingying Wan","slug":"yingying-wan","given":"Yingying","family":"Wan","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Junbo Ma","slug":"junbo-ma","given":"Junbo","family":"Ma","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Debo Cheng","slug":"debo-cheng","given":"Debo","family":"Cheng","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China; School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu 611731 , China"}],"abstract":"Although meta-learning and metric learning have been widely applied for few-shot node classification (FSNC), some limitations still need to be addressed, such as expensive time costs for the meta-train and difficult of exploring the complex structure inherent the graph data. To address in issues, this paper proposes a new data augmentation method to conduct FSNC on the graph data including parameter initialization and parameter fine-tuning. Specifically, parameter initialization only conducts a multi-classification task on the base classes, resulting in good generalization ability and less time cost. Parameter fine-tuning designs two data augmentation methods (i.e., support augmentation and shot augmentation) on the novel classes to generate sufficient node features so that any traditional supervised classifiers can be used to classify the query set. As a result, the proposed method is the first work of data augmentation for FSNC. Experiment results show the effectiveness and the efficiency of our proposed method, compared to state-of-the-art methods, in terms of different classification tasks.","doi":"10.24963/ijcai.2022/500","url":"https://ijcai.org/proceedings/2022/500","pdf":"https://ijcai.org/proceedings/2022/0500.pdf","markdown":"https://ijcai.org/proceedings/2022/500.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/500","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":500,"pages":{"first":3601,"last":3607},"keywords":["Machine Learning: Semi-Supervised Learning","Machine Learning: Convolutional Networks","Machine Learning: Few-shot learning","Machine Learning: Classification"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986829","length_sec":69,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984972","length_sec":200,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Cheng et al., 2019] Yu Cheng, Mo Yu, Xiaoxiao Guo, and Bowen Zhou. 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