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Web"],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            This article studies the problem of semi-supervised learning on graphs, which aims to incorporate ubiquitous unlabeled knowledge (e.g., graph topology, node attributes) with few-available labeled knowledge (e.g., node class) to alleviate the scarcity issue of supervised information on node classification. While promising results are achieved, existing works for this problem usually suffer from the poor balance of generalization and fitting ability due to the heavy reliance on labels or task-agnostic unsupervised information. To address the challenge, we propose a dual-channel framework for semi-supervised learning on\n            <jats:bold>G<\/jats:bold>\n            raphs via\n            <jats:bold>K<\/jats:bold>\n            nowledge\n            <jats:bold>T<\/jats:bold>\n            ransfer between independent supervised and unsupervised embedding spaces, namely, GKT. Specifically, we devise a dual-channel framework including a supervised model for learning the label probability of nodes and an unsupervised model for extracting information from massive unlabeled graph data. A knowledge transfer head is proposed to bridge the gap between the generalization and fitting capability of the two models. We use the unsupervised information to reconstruct batch-graphs to smooth the label probability distribution on the graphs to improve the generalization of prediction. We also adaptively adjust the reconstructed graphs by encouraging the label-related connections to solidify the fitting ability. Since the optimization of the supervised channel with knowledge transfer contains that of the unsupervised channel as a constraint and vice versa, we then propose a meta-learning-based method to solve the bi-level optimization problem, which avoids the negative transfer and further improves the model\u2019s performance. Finally, extensive experiments validate the effectiveness of our proposed framework by comparing state-of-the-art algorithms.\n          <\/jats:p>","DOI":"10.1145\/3577033","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T11:30:37Z","timestamp":1674041437000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Dual-channel Semi-supervised Learning Framework on Graphs via Knowledge Transfer and Meta-learning"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-4861","authenticated-orcid":false,"given":"Ziyue","family":"Qiao","sequence":"first","affiliation":[{"name":"Jiangmen Laboratory of Carbon Science and Technology, China; Computer Network Information Center, Chinese Academy of Sciences; and Guangzhou HKUST Fok Ying Tung Research Institute"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-5523","authenticated-orcid":false,"given":"Pengyang","family":"Wang","sequence":"additional","affiliation":[{"name":"SKL-IOTSC, University of Macau, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1075-0684","authenticated-orcid":false,"given":"Pengfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, CAS, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4852-0163","authenticated-orcid":false,"given":"Zhiyuan","family":"Ning","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, CAS, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-8024","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-8937","authenticated-orcid":false,"given":"Yi","family":"Du","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, CAS, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2144-1131","authenticated-orcid":false,"given":"Yuanchun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, CAS, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4454-7919","authenticated-orcid":false,"given":"Jianqiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Damo Academy, Alibaba Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-5049","authenticated-orcid":false,"given":"Xian-Sheng","family":"Hua","sequence":"additional","affiliation":[{"name":"Damo Academy, Alibaba Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology (Guangzhou) and Guangzhou HKUST Fok Ying Tung Research Institute, China"}]}],"member":"320","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v12i1.15047"},{"key":"e_1_3_2_3_2","article-title":"Ripple walk training: A subgraph-based training framework for large and deep graph neural network","author":"Bai Jiyang","year":"2020","unstructured":"Jiyang Bai, Yuxiang Ren, and Jiawei Zhang. 2020. 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