{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T06:07:23Z","timestamp":1774332443079,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T00:00:00Z","timestamp":1702857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T00:00:00Z","timestamp":1702857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U21A20473"],"award-info":[{"award-number":["No. U21A20473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272285"],"award-info":[{"award-number":["62272285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306205"],"award-info":[{"award-number":["62306205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Program of Shanxi Province","award":["202203021222189"],"award-info":[{"award-number":["202203021222189"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s13042-023-02046-7","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T16:02:40Z","timestamp":1702915360000},"page":"2527-2538","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Debiased graph contrastive learning based on positive and unlabeled learning"],"prefix":"10.1007","volume":"15","author":[{"given":"Zhiqiang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiye","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,18]]},"reference":[{"issue":"5","key":"2046_CR1","doi-asserted-by":"publisher","first-page":"6017","DOI":"10.1007\/s12652-020-02623-6","volume":"14","author":"AA Movassagh","year":"2023","unstructured":"Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N (2023) Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Humaniz Comput 14(5):6017\u20136025","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"2","key":"2046_CR2","doi-asserted-by":"publisher","first-page":"2969","DOI":"10.3233\/JIFS-190078","volume":"37","author":"R Jain","year":"2019","unstructured":"Jain R, Alzubi JA, Jain N, Joshi P (2019) Assessing risk in life insurance using ensemble learning. J Intell Fuzzy Syst 37(2):2969\u20132980","journal-title":"J Intell Fuzzy Syst"},{"issue":"7","key":"2046_CR3","doi-asserted-by":"publisher","first-page":"4069","DOI":"10.1002\/ett.4069","volume":"32","author":"JA Alzubi","year":"2021","unstructured":"Alzubi JA (2021) Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks. Trans Emerg Telecommun Technol 32(7):4069","journal-title":"Trans Emerg Telecommun Technol"},{"issue":"2","key":"2046_CR4","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1007\/s10586-022-03686-0","volume":"26","author":"OA Alzubi","year":"2023","unstructured":"Alzubi OA, Qiqieh I, Alzubi JA (2023) Fusion of deep learning based cyberattack detection and classification model for intelligent systems. Clust Comput 26(2):1363\u20131374","journal-title":"Clust Comput"},{"key":"2046_CR5","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: Proceedings of the 34th international conference on machine learning. Sydney, NSW, Australia, pp 1263\u20131272"},{"issue":"8","key":"2046_CR6","doi-asserted-by":"publisher","first-page":"2239","DOI":"10.1007\/s13042-022-01520-y","volume":"13","author":"F Yang","year":"2022","unstructured":"Yang F, Zhang H, Tao S (2022) Hybrid deep graph convolutional networks. Int J Mach Learn Cybern 13(8):2239\u20132255","journal-title":"Int J Mach Learn Cybern"},{"issue":"7","key":"2046_CR7","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1007\/s13042-022-01764-8","volume":"14","author":"D Li","year":"2023","unstructured":"Li D, Miao S, Zhao B, Zhou Y, Feng D, Zhao J, Niu X (2023) Convhia: convolutional network with hierarchical attention for knowledge graph multi-hop reasoning. Int J Mach Learn Cybern 14(7):2301\u20132315","journal-title":"Int J Mach Learn Cybern"},{"key":"2046_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-023-01796-8","author":"J Yanmei","year":"2023","unstructured":"Yanmei J, Mingsheng L, Yangyang L, Yaping L, Jingyun Z, Yifeng L, Chunyang L (2023) Enhanced neighborhood node graph neural networks for load forecasting in smart grid. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-023-01796-8","journal-title":"Int J Mach Learn Cybern"},{"key":"2046_CR9","doi-asserted-by":"crossref","unstructured":"Gao H, Pei J, Huang H (2019) Progan: network embedding via proximity generative adversarial network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, Anchorage, AK, USA, pp 1308\u20131316","DOI":"10.1145\/3292500.3330866"},{"key":"2046_CR10","unstructured":"Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: Proceedings of the 37th international conference on machine learning, Virtual Event, pp 1725\u20131735"},{"key":"2046_CR11","doi-asserted-by":"crossref","unstructured":"Chen X, Xie S, He K (2021) An empirical study of training self-supervised vision transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, Montreal, QC, Canada, pp 9640\u20139649","DOI":"10.1109\/ICCV48922.2021.00950"},{"issue":"10","key":"2046_CR12","doi-asserted-by":"publisher","first-page":"3413","DOI":"10.1007\/s13042-023-01840-7","volume":"14","author":"W Fan","year":"2023","unstructured":"Fan W, Shangguan W, Chen Y (2023) Transformer-based contrastive learning framework for image anomaly detection. Int J Mach Learn Cybern 14(10):3413\u20133426","journal-title":"Int J Mach Learn Cybern"},{"issue":"5","key":"2046_CR13","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1007\/s13042-022-01719-z","volume":"14","author":"H Wang","year":"2023","unstructured":"Wang H, Zhang T, Zhang X (2023) Contrastive embedding-based feature generation for generalized zero-shot learning. Int J Mach Learn Cybern 14(5):1669\u20131681","journal-title":"Int J Mach Learn Cybern"},{"key":"2046_CR14","doi-asserted-by":"crossref","unstructured":"Peng Z, Huang W, Luo M, Zheng Q, Rong Y, Xu T, Huang J (2020) Graph representation learning via graphical mutual information maximization. In: Proceedings of the web conference, Taipei, Taiwan, pp 259\u2013270","DOI":"10.1145\/3366423.3380112"},{"key":"2046_CR15","unstructured":"Hassani K, Khasahmadi AH (2020) Contrastive multi-view representation learning on graphs. In: Proceedings of the 37th international conference on machine learning, Virtual Event, pp 4116\u20134126"},{"key":"2046_CR16","unstructured":"Li H, Wang X, Zhang Z, Yuan Z, Li H, Zhu W (2021) Disentangled contrastive learning on graphs. In: Proceedings of the 34th advances in neural information processing systems, Virtual Event, pp 21872\u201321884"},{"key":"2046_CR17","unstructured":"You Y, Chen T, Sui Y, Chen T, Wang Z, Shen, Y (2020) Graph contrastive learning with augmentations. In: Proceedings of the 33th advances in neural information processing systems, Virtual Event, pp 5812\u20135823"},{"key":"2046_CR18","doi-asserted-by":"crossref","unstructured":"Ding M, Tang J, Zhang J (2018) Semi-supervised learning on graphs with generative adversarial nets. In: Proceedings of the 27th ACM international conference on information and knowledge management, Torino, Italy, pp 913\u2013922","DOI":"10.1145\/3269206.3271768"},{"key":"2046_CR19","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.neunet.2023.04.001","volume":"163","author":"H Liang","year":"2023","unstructured":"Liang H, Du X, Zhu B, Ma Z, Chen K, Gao J (2023) Graph contrastive learning with implicit augmentations. Neural Netw 163:156\u2013164","journal-title":"Neural Netw"},{"issue":"3","key":"2046_CR20","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1007\/s13042-022-01677-6","volume":"14","author":"C Cui","year":"2023","unstructured":"Cui C, Wang J, Wei W, Liang J (2023) Hybrid sampling-based contrastive learning for imbalanced node classification. Int J Mach Learn Cybern 14(3):989\u20131001","journal-title":"Int J Mach Learn Cybern"},{"issue":"11","key":"2046_CR21","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1109\/TPAMI.2018.2860995","volume":"41","author":"E Sansone","year":"2018","unstructured":"Sansone E, De Natale FG, Zhou Z-H (2018) Efficient training for positive unlabeled learning. IEEE Trans Pattern Anal Mach Intell 41(11):2584\u20132598","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"2046_CR22","doi-asserted-by":"publisher","first-page":"3471","DOI":"10.1109\/TNNLS.2019.2892403","volume":"30","author":"C Gong","year":"2019","unstructured":"Gong C, Liu T, Yang J, Tao D (2019) Large-margin label-calibrated support vector machines for positive and unlabeled learning. IEEE Trans Neural Netw Learn Syst 30(11):3471\u20133483","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"8","key":"2046_CR23","first-page":"4163","volume":"44","author":"C Gong","year":"2021","unstructured":"Gong C, Wang Q, Liu T, Han B, You J, Yang J, Tao D (2021) Instance-dependent positive and unlabeled learning with labeling bias estimation. IEEE Trans Pattern Anal Mach Intell 44(8):4163\u20134177","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2046_CR24","unstructured":"Kato M, Teshima T, Honda J (2019) Learning from positive and unlabeled data with a selection bias. In: Proceedings of the 36th international conference on machine learning. ICLR (Poster), New Orleans, LA, USA"},{"key":"2046_CR25","doi-asserted-by":"crossref","unstructured":"Bekker J, Robberechts P, Davis J (2019) Beyond the selected completely at random assumption for learning from positive and unlabeled data. In: Proceedings of machine learning and knowledge discovery in databases: European conference, W\u00fcrzburg, Germany, pp 71\u201385","DOI":"10.1007\/978-3-030-46147-8_5"},{"key":"2046_CR26","unstructured":"Du Plessis M, Niu G, Sugiyama M (2015) Convex formulation for learning from positive and unlabeled data. In: Proceedings of the 32th international conference on machine learning, Lille, France, pp 1386\u20131394"},{"key":"2046_CR27","unstructured":"Kiryo R, Niu G, Du Plessis MC, Sugiyama M (2017) Positive-unlabeled learning with non-negative risk estimator. In: Proceedings of the 31th advances in neural information processing systems, Long Beach, CA, USA, pp 1675\u20131685"},{"key":"2046_CR28","unstructured":"Zhu Y, Xu Y, Liu Q, Wu S (2021) An empirical study of graph contrastive learning. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, Virtual Event"},{"key":"2046_CR29","unstructured":"Hassani K, Khasahmadi AH (2020) Contrastive multi-view representation learning on graphs. In: Proceedings of the 37th international conference on machine learning, Virtual Event, pp 4116\u20134126"},{"key":"2046_CR30","unstructured":"Sun F, Hoffmann J, Verma V, Tang J (2020) Infograph: unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In: Proceedings of 8th international conference on learning representations. ICLR 2020, Addis Ababa, Ethiopia"},{"issue":"9","key":"2046_CR31","doi-asserted-by":"publisher","first-page":"2979","DOI":"10.1007\/s13042-023-01813-w","volume":"14","author":"X Ma","year":"2023","unstructured":"Ma X, Li H, Shi J, Zhang Y, Long Z (2023) Importance-aware contrastive learning via semantically augmented instances for unsupervised sentence embeddings. Int J Mach Learn Cybern 14(9):2979\u20132990","journal-title":"Int J Mach Learn Cybern"},{"issue":"10","key":"2046_CR32","doi-asserted-by":"publisher","first-page":"3413","DOI":"10.1007\/s13042-023-01840-7","volume":"14","author":"W Fan","year":"2023","unstructured":"Fan W, Shangguan W, Chen Y (2023) Transformer-based contrastive learning framework for image anomaly detection. Int J Mach Learn Cybern 14(10): 3413\u20133426","journal-title":"nt J Mach Learn Cybern"},{"issue":"9","key":"2046_CR33","doi-asserted-by":"publisher","first-page":"2415","DOI":"10.1007\/s13042-022-01533-7","volume":"13","author":"X Zhang","year":"2022","unstructured":"Zhang X, Wang S, Wu Z, Tan X (2022) Unsupervised image clustering algorithm based on contrastive learning and k-nearest neighbors. Int J Mach Learn Cybern 13(9):2415\u20132423","journal-title":"Int J Mach Learn Cybern"},{"issue":"9","key":"2046_CR34","doi-asserted-by":"publisher","first-page":"3105","DOI":"10.1007\/s13042-023-01823-8","volume":"14","author":"L Wu","year":"2023","unstructured":"Wu L, Hu J, Teng F, Li T, Du S (2023) Text semantic matching with an enhanced sample building method based on contrastive learning. Int J Mach Learn Cybern 14(9):3105\u20133112","journal-title":"Int J Mach Learn Cybern"},{"issue":"9","key":"2046_CR35","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1007\/s13042-023-01831-8","volume":"14","author":"H Xiong","year":"2023","unstructured":"Xiong H, Yan Z, Wu C, Lu G, Pang S, Xue Y, Cai Q (2023) Bart-based contrastive and retrospective network for aspect-category-opinion-sentiment quadruple extraction. Int J Mach Learn Cybern 14(9):3243\u20133255","journal-title":"Int J Mach Learn Cybern"},{"key":"2046_CR36","unstructured":"Velickovic P (2019) Fedus: deep graph infomax. In: Proceedings of 7th international conference on learning representations. ICLR (Poster) 2019, New Orleans, LA, USA"},{"key":"2046_CR37","doi-asserted-by":"crossref","unstructured":"Qiu J, Chen Q, Dong Y, Zhang J, Yang H, Ding M, Wang K, Tang J (2020) Gcc: graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining, Virtual Conference, USA, pp 1150\u20131160","DOI":"10.1145\/3394486.3403168"},{"key":"2046_CR38","unstructured":"You Y, Chen T, Sui Y, Chen T, Wang Z, Shen Y (2020) Graph contrastive learning with augmentations. In: Proceedings of the 33th advances in neural information processing systems, Virtual Event, pp 5812\u20135823"},{"key":"2046_CR39","unstructured":"Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2020) Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131"},{"key":"2046_CR40","doi-asserted-by":"crossref","unstructured":"Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2021) Graph contrastive learning with adaptive augmentation. In: Proceedings of the web conference, Virtual Event \/ Ljubljana, Slovenia, pp 2069\u20132080","DOI":"10.1145\/3442381.3449802"},{"key":"2046_CR41","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th international conference on machine learning, Virtual Event, pp 1597\u20131607"},{"key":"2046_CR42","unstructured":"Chen T-S, Hung W-C, Tseng H-Y, Chien S-Y, Yang M-H (2021) Incremental false negative detection for contrastive learning. In: Proceedings of 10th international conference on learning representations. ICLR 2022, Virtual Event"},{"key":"2046_CR43","doi-asserted-by":"crossref","unstructured":"Qin X, Sheikh N, Reinwald B, Wu L (2021) Relation-aware graph attention model with adaptive self-adversarial training. In: Proceedings of the AAAI conference on artificial intelligence, Virtual Event, pp 9368\u20139376","DOI":"10.1609\/aaai.v35i11.17129"},{"key":"2046_CR44","doi-asserted-by":"crossref","unstructured":"Zhao H, Yang X, Wang Z, Yang E, Deng C (2021) Graph debiased contrastive learning with joint representation clustering. In: Proceedings of the 30th international joint conference on artificial intelligence, Virtual Event \/ Montreal, Canada, pp 3434\u20133440","DOI":"10.24963\/ijcai.2021\/473"},{"key":"2046_CR45","doi-asserted-by":"crossref","unstructured":"Chu G, Wang X, Shi C, Jiang X (2021) Cuco: graph representation with curriculum contrastive learning. In: Proceedings of the 30th international joint conference on artificial intelligence, Virtual Event \/ Montreal, Canada, pp 2300\u20132306","DOI":"10.24963\/ijcai.2021\/317"},{"key":"2046_CR46","doi-asserted-by":"crossref","unstructured":"Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, Las Vegas, Nevada, USA, pp 213\u2013220","DOI":"10.1145\/1401890.1401920"},{"key":"2046_CR47","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, New York City, USA,  pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"2046_CR48","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308"},{"key":"2046_CR49","unstructured":"Velickovic P (2018) Cucurull: graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"2046_CR50","unstructured":"Xia J, Wu L, Wang G, Chen J, Li SZ (2022) Progcl: rethinking hard negative mining in graph contrastive learning. In: Proceedings of the 39th international conference on machine learning, Baltimore, MD, USA, pp 24332\u201324346"},{"key":"2046_CR51","unstructured":"Ng A, Jordan M, Weiss Y (2001) On spectral clustering: analysis and an algorithm. In: Proceedings of the 14th advances in neural information processing systems, Vancouver, British Columbia, Canada, pp 849\u2013856"},{"key":"2046_CR52","doi-asserted-by":"crossref","unstructured":"Pan S, Hu R, Long G, Jiang J, Yao L, Zhang C (2018) Adversarially regularized graph autoencoder for graph embedding. In: Proceedings of the 27th international joint conference on artificial intelligence, Stockholm, Sweden, pp 2609\u20132615","DOI":"10.24963\/ijcai.2018\/362"},{"key":"2046_CR53","doi-asserted-by":"crossref","unstructured":"Jin M, Zheng Y, Li Y, Gong C, Zhou C, Pan S (2021) Multi-scale contrastive siamese networks for self-supervised graph representation learning. In: Proceedings of the 30th international joint conference on artificial intelligence, Virtual Event \/ Montreal, Canada, pp 1477\u20131483","DOI":"10.24963\/ijcai.2021\/204"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02046-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02046-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02046-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T05:31:13Z","timestamp":1716442273000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02046-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,18]]},"references-count":53,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["2046"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02046-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,18]]},"assertion":[{"value":"26 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}