{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:13:35Z","timestamp":1775578415729,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/253","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"2279-2287","source":"Crossref","is-referenced-by-count":13,"title":["Semi-supervised Domain Adaptation in Graph Transfer Learning"],"prefix":"10.24963","author":[{"given":"Ziyue","family":"Qiao","sequence":"first","affiliation":[{"name":"Jiangmen Laboratory of Carbon Science and Technology, Jiangmen;"},{"name":"The Hong Kong University of Science and Technology (Guangzhou);"},{"name":"Guangzhou HKUST Fok Ying Tung Research Institute"}]},{"given":"Xiao","family":"Luo","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou)"}]},{"given":"Meng","family":"Xiao","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences;"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Hao","family":"Dong","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences;"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Yuanchun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences;"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou);"},{"name":"Guangzhou HKUST Fok Ying Tung Research Institute"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:43:01Z","timestamp":1691743381000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/253"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/253","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}