{"type":"ScholarlyArticle","title":"Semi-supervised Domain Adaptation in Graph Transfer Learning","authors":[{"name":"Ziyue Qiao","slug":"ziyue-qiao","given":"Ziyue","family":"Qiao","affiliation":"Jiangmen Laboratory of Carbon Science and Technology , Jiangmen; The Hong Kong University of Science and Technology (Guangzhou) , Guangzhou; Guangzhou HKUST Fok Ying Tung Research Institute , Guangzhou"},{"name":"Xiao Luo","slug":"xiao-luo","given":"Xiao","family":"Luo","affiliation":"The Hong Kong University of Science and Technology (Guangzhou) , Guangzhou"},{"name":"Meng Xiao","slug":"meng-xiao","given":"Meng","family":"Xiao","affiliation":"Computer Network Information Center , Chinese Academy of Sciences , Beijing; University of Chinese Academy of Sciences , Beijing"},{"name":"Hao Dong","slug":"hao-dong","given":"Hao","family":"Dong","affiliation":"Computer Network Information Center , Chinese Academy of Sciences , Beijing; University of Chinese Academy of Sciences , Beijing"},{"name":"Yuanchun Zhou","slug":"yuanchun-zhou","given":"Yuanchun","family":"Zhou","affiliation":"Computer Network Information Center , Chinese Academy of Sciences , Beijing; University of Chinese Academy of Sciences , Beijing"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"The Hong Kong University of Science and Technology (Guangzhou) , Guangzhou; Guangzhou HKUST Fok Ying Tung Research Institute , Guangzhou"}],"abstract":"As a specific case of graph transfer learning, unsupervised domain adaptation on graphs aims for knowledge transfer from label-rich source graphs to unlabeled target graphs. However, graphs with topology and attributes usually have considerable cross-domain disparity and there are numerous real-world scenarios where merely a subset of nodes are labeled in the source graph. This imposes critical challenges on graph transfer learning due to serious domain shifts and label scarcity. To address these challenges, we propose a method named Semi-supervised Graph Domain Adaptation (SGDA). To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding. Thus, the node classifier trained on labeled source nodes can be transferred to the target nodes. Moreover, to address the label scarcity, we propose pseudo-labeling on unlabeled nodes, which improves classification on the target graph via measuring the posterior influence of nodes based on their relative position to the class centroids. Finally, extensive experiments on a range of publicly accessible datasets validate the effectiveness of our proposed SGDA in different experimental settings.","doi":"10.24963/ijcai.2023/253","url":"https://ijcai.org/proceedings/2023/253","pdf":"https://ijcai.org/proceedings/2023/0253.pdf","markdown":"https://ijcai.org/proceedings/2023/253.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/253","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":"Data Mining","track":"Main Track","number":253,"pages":{"first":2279,"last":2287},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Semi-supervised learning","Machine Learning: ML: Multi-task and transfer learning"],"videos":[],"references":[{"position":1,"raw":"[Chang et al., 2021] Ji Chang, Jing Li, Yu Kang, Wenjun Lv, Ting Xu, Zerui Li, Wei Xing Zheng, Hongwei Han, and Haining Liu. 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