{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T19:24:56Z","timestamp":1778786696335,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92270106"],"award-info":[{"award-number":["92270106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["16200021"],"award-info":[{"award-number":["16200021"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3615250","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"4390-4394","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Positive-Unlabeled Node Classification with Structure-aware Graph Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0479-9898","authenticated-orcid":false,"given":"Hansi","family":"Yang","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2085-7418","authenticated-orcid":false,"given":"Yongqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"4Paradigm, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8944-8618","authenticated-orcid":false,"given":"Quanming","family":"Yao","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4828-8248","authenticated-orcid":false,"given":"James","family":"Kwok","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Mixmatch: A holistic approach to semi-supervised learning. NeurIPS","author":"Berthelot David","year":"2019","unstructured":"David Berthelot , Nicholas Carlini , Ian Goodfellow , Nicolas Papernot , Avital Oliver , and Colin A Raffel . 2019 . Mixmatch: A holistic approach to semi-supervised learning. NeurIPS (2019). David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. 2019. Mixmatch: A holistic approach to semi-supervised learning. NeurIPS (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Self-pu: Self boosted and calibrated positive-unlabeled training. In ICML.","author":"Chen Xuxi","year":"2020","unstructured":"Xuxi Chen , Wuyang Chen , Tianlong Chen , Ye Yuan , Chen Gong , Kewei Chen , and Zhangyang Wang . 2020 . Self-pu: Self boosted and calibrated positive-unlabeled training. In ICML. Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, and Zhangyang Wang. 2020. Self-pu: Self boosted and calibrated positive-unlabeled training. In ICML."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Rui Dai Shenkun Xu Qian Gu Chenguang Ji and Kaikui Liu. 2020. Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data.. In SIGKDD.  Rui Dai Shenkun Xu Qian Gu Chenguang Ji and Kaikui Liu. 2020. Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data.. In SIGKDD.","DOI":"10.1145\/3394486.3403358"},{"key":"e_1_3_2_2_4_1","unstructured":"Marthinus Christoffel du Plessis Gang Niu and Masashi Sugiyama. 2014. Analysis of learning from positive and unlabeled data. In NeurIPS.  Marthinus Christoffel du Plessis Gang Niu and Masashi Sugiyama. 2014. Analysis of learning from positive and unlabeled data. In NeurIPS."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Yuan Fang Bo-June Hsu and Kevin Chen-Chuan Chang. 2012. Confidence-aware graph regularization with heterogeneous pairwise features. In SIGIR.  Yuan Fang Bo-June Hsu and Kevin Chen-Chuan Chang. 2012. Confidence-aware graph regularization with heterogeneous pairwise features. In SIGIR.","DOI":"10.1145\/2348283.2348410"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3061456"},{"key":"e_1_3_2_2_7_1","unstructured":"Yu-Guan Hsieh Gang Niu and Masashi Sugiyama. 2019. Classification from positive unlabeled and biased negative data. In ICML.  Yu-Guan Hsieh Gang Niu and Masashi Sugiyama. 2019. Classification from positive unlabeled and biased negative data. In ICML."},{"key":"e_1_3_2_2_8_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.  Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_9_1","volume-title":"Marthinus Christoffel du Plessis, and Masashi Sugiyama","author":"Kiryo Ryuichi","year":"2017","unstructured":"Ryuichi Kiryo , Gang Niu , Marthinus Christoffel du Plessis, and Masashi Sugiyama . 2017 . Positive-unlabeled learning with non-negative risk estimator. In NeurIPS. Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, and Masashi Sugiyama. 2017. Positive-unlabeled learning with non-negative risk estimator. In NeurIPS."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Chuan Luo Pu Zhao Chen Chen Bo Qiao Chao Du Hongyu Zhang Wei Wu Shaowei Cai Bing He Saravanakumar Rajmohan and Qingwei Lin. 2021. PULNS: positive-unlabeled learning with effective negative sample selector. In AAAI.  Chuan Luo Pu Zhao Chen Chen Bo Qiao Chao Du Hongyu Zhang Wei Wu Shaowei Cai Bing He Saravanakumar Rajmohan and Qingwei Lin. 2021. PULNS: positive-unlabeled learning with effective negative sample selector. In AAAI.","DOI":"10.1609\/aaai.v35i10.17064"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"George Panagopoulos Giannis Nikolentzos and Michalis Vazirgiannis. 2021. Transfer graph neural networks for pandemic forecasting. In AAAI.  George Panagopoulos Giannis Nikolentzos and Michalis Vazirgiannis. 2021. Transfer graph neural networks for pandemic forecasting. In AAAI.","DOI":"10.1609\/aaai.v35i6.16616"},{"key":"e_1_3_2_2_12_1","unstructured":"Boon-Siew Seah Aixin Sun and Sourav S Bhowmick. 2018. Killing two birds with one stone: Concurrent ranking of tags and comments of social images. In SIGIR.  Boon-Siew Seah Aixin Sun and Sourav S Bhowmick. 2018. Killing two birds with one stone: Concurrent ranking of tags and comments of social images. In SIGIR."},{"key":"e_1_3_2_2_13_1","volume-title":"Collective classification in network data. AI magazine","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen , Galileo Namata , Mustafa Bilgic , Lise Getoor , Brian Galligher , and Tina Eliassi-Rad . 2008. Collective classification in network data. AI magazine , Vol. 29 , 3 ( 2008 ), 93--93. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine, Vol. 29, 3 (2008), 93--93."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Jie Tang Jing Zhang Limin Yao Juanzi Li Li Zhang and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In SIGKDD. 990--998.  Jie Tang Jing Zhang Limin Yao Juanzi Li Li Zhang and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In SIGKDD. 990--998.","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_2_15_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. ICLR.  Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. ICLR."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Weiqing Wang Hongzhi Yin Xingzhong Du Wen Hua Yongjun Li and Quoc Viet Hung Nguyen. 2019. Online user representation learning across heterogeneous social networks. In SIGIR.  Weiqing Wang Hongzhi Yin Xingzhong Du Wen Hua Yongjun Li and Quoc Viet Hung Nguyen. 2019. Online user representation learning across heterogeneous social networks. In SIGIR.","DOI":"10.1145\/3331184.3331258"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450316"},{"key":"e_1_3_2_2_18_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR.  Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR."},{"key":"e_1_3_2_2_19_1","unstructured":"Jaemin Yoo Junghun Kim Hoyoung Yoon Geonsoo Kim Changwon Jang and U Kang. 2021. Accurate graph-based PU learning without class prior. In ICDM.  Jaemin Yoo Junghun Kim Hoyoung Yoon Geonsoo Kim Changwon Jang and U Kang. 2021. Accurate graph-based PU learning without class prior. In ICDM."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Yunrui Zhao Qianqian Xu Yangbangyan Jiang Peisong Wen and Qingming Huang. 2022. Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective. In CVPR.  Yunrui Zhao Qianqian Xu Yangbangyan Jiang Peisong Wen and Qingming Huang. 2022. Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective. In CVPR.","DOI":"10.1109\/CVPR52688.2022.01406"},{"key":"e_1_3_2_2_21_1","volume-title":"Semi-supervised learning. Machine Learning","author":"Zhou Zhi-Hua","year":"2021","unstructured":"Zhi-Hua Zhou . 2021. Semi-supervised learning. Machine Learning ( 2021 ), 315--341. Zhi-Hua Zhou. 2021. Semi-supervised learning. Machine Learning (2021), 315--341."}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615250","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615250","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:58Z","timestamp":1750178218000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":21,"alternative-id":["10.1145\/3583780.3615250","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3615250","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}