{"type":"ScholarlyArticle","title":"Joint Domain Adaptive Graph Convolutional Network","authors":[{"name":"Niya Yang","slug":"niya-yang","given":"Niya","family":"Yang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Ye Wang","slug":"ye-wang","given":"Ye","family":"Wang","affiliation":"Changchun Institute of Optics , Fine Mechanics and Physics , Chinese Academy of Sciences , Changchun , China"},{"name":"Zhizhi Yu","slug":"zhizhi-yu","given":"Zhizhi","family":"Yu","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Xin Huang","slug":"xin-huang","given":"Xin","family":"Huang","affiliation":"Department of Computer Science , Hong Kong Baptist University , Hong Kong , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"}],"abstract":"In the realm of cross-network tasks, graph domain adaptation is an effective tool due to its ability to transfer abundant labels from nodes in the source domain to those in the target domain. Existing adversarial domain adaptation methods mainly focus on domain-wise alignment. These approaches, while effective in mitigating the marginal distribution shift between the two domains, often ignore the integral aspect of structural alignment, potentially leading to negative transfer. To address this issue, we propose a joint adversarial domain adaptive graph convolutional network (JDA-GCN) that is uniquely augmented with structural graph alignment, so as to enhance the efficacy of knowledge transfer. Specifically, we construct a structural graph to delineate the interconnections among nodes within identical categories across the source and target domains. To further refine node representation, we integrate the local consistency matrix with the global consistency matrix, thereby leveraging the learning of the sub-structure similarity of nodes to enable more robust and effective representation of nodes. Empirical evaluation on diverse real-world datasets substantiates the superiority of our proposed method, marking a significant advancement over existing state-of-the-art graph domain adaptation algorithms.","doi":"10.24963/ijcai.2024/276","url":"https://ijcai.org/proceedings/2024/276","pdf":"https://ijcai.org/proceedings/2024/0276.pdf","markdown":"https://ijcai.org/proceedings/2024/276.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/276","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":276,"pages":{"first":2496,"last":2504},"keywords":["Machine Learning: ML: Sequence and graph learning","Data Mining: DM: Mining graphs","Machine Learning: ML: Classification"],"videos":[],"references":[{"position":1,"raw":"[Beigi and Moattar, 2021] Omid Mohamad Beigi and Mo- hammad H Moattar. 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