{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T19:01:12Z","timestamp":1784314872818,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,13]]},"DOI":"10.1145\/3726302.3730007","type":"proceedings-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T01:25:28Z","timestamp":1752456328000},"page":"719-728","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["InfoNCE is a Free Lunch for Semantically guided Graph Contrastive Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1327-6366","authenticated-orcid":false,"given":"Zixu","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0147-2590","authenticated-orcid":false,"given":"Bingbing","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8856-668X","authenticated-orcid":false,"given":"Yige","family":"Yuan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2425-1499","authenticated-orcid":false,"given":"Huawei","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5201-8195","authenticated-orcid":false,"given":"Xueqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Positive unlabeled contrastive learning. arXiv preprint arXiv:2206.01206","author":"Acharya Anish","year":"2022","unstructured":"Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael Rabbat, and Inderjit Dhillon. 2022. Positive unlabeled contrastive learning. arXiv preprint arXiv:2206.01206 (2022)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0076027"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109631"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_28"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672035"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/276675.276685"},{"key":"e_1_3_2_1_7_1","first-page":"2059","article-title":"Good: A graph out-of-distribution benchmark","volume":"35","author":"Gui Shurui","year":"2022","unstructured":"Shurui Gui, Xiner Li, Limei Wang, and Shuiwang Ji. 2022. Good: A graph out-of-distribution benchmark. Advances in Neural Information Processing Systems, Vol. 35 (2022), 2059-2073.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_8_1","first-page":"13088","article-title":"Learning from positive and unlabeled data with arbitrary positive shift","volume":"33","author":"Hammoudeh Zayd","year":"2020","unstructured":"Zayd Hammoudeh and Daniel Lowd. 2020. Learning from positive and unlabeled data with arbitrary positive shift. Advances in Neural Information Processing Systems, Vol. 33 (2020), 13088-13099.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","volume-title":"International conference on machine learning. PMLR, 4116-4126","author":"Hassani Kaveh","year":"2020","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In International conference on machine learning. PMLR, 4116-4126."},{"key":"e_1_3_2_1_10_1","volume-title":"Statistical outlier detection using direct density ratio estimation. Knowledge and information systems","author":"Hido Shohei","year":"2011","unstructured":"Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, and Takafumi Kanamori. 2011. Statistical outlier detection using direct density ratio estimation. Knowledge and information systems, Vol. 26 (2011), 309-336."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403237"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/IISA.2019.8900698"},{"key":"e_1_3_2_1_13_1","volume-title":"Self-supervised learning on graphs: Deep insights and new direction. arXiv preprint arXiv:2006.10141","author":"Jin Wei","year":"2020","unstructured":"Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang. 2020. Self-supervised learning on graphs: Deep insights and new direction. arXiv preprint arXiv:2006.10141 (2020)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449971"},{"key":"e_1_3_2_1_15_1","unstructured":"Wei Ju Yifan Wang Yifang Qin Zhengyang Mao Zhiping Xiao Junyu Luo Junwei Yang Yiyang Gu Dongjie Wang Qingqing Long et al. 2024. Towards Graph Contrastive Learning: A Survey and Beyond. arXiv preprint arXiv:2405.11868 (2024)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1755831"},{"key":"e_1_3_2_1_17_1","volume-title":"International conference on learning representations.","author":"Kato Masahiro","year":"2019","unstructured":"Masahiro Kato, Takeshi Teshima, and Junya Honda. 2019. Learning from positive and unlabeled data with a selection bias. In International conference on learning representations."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-023-02046-7"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539347"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1489-1500","author":"Liu Mengyue","year":"2023","unstructured":"Mengyue Liu, Yun Lin, Jun Liu, Bohao Liu, Qinghua Zheng, and Jin Song Dong. 2023. B2-sampling: Fusing balanced and biased sampling for graph contrastive learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1489-1500."},{"key":"e_1_3_2_1_21_1","volume-title":"Graph self-supervised learning: A survey","author":"Liu Yixin","year":"2022","unstructured":"Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and S Yu Philip. 2022a. Graph self-supervised learning: A survey. IEEE transactions on knowledge and data engineering, Vol. 35, 6 (2022), 5879-5900."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75762-5_57"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_1_24_1","volume-title":"Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901","author":"Mernyei P\u00e9ter","year":"2020","unstructured":"P\u00e9ter Mernyei and Catalina Cangea. 2020. Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901 (2020)."},{"key":"e_1_3_2_1_25_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_1_26_1","volume-title":"Self-supervised graph representation learning via global context prediction. arXiv preprint arXiv:2003.01604","author":"Peng Zhen","year":"2020","unstructured":"Zhen Peng, Yixiang Dong, Minnan Luo, Xiao-Ming Wu, and Qinghua Zheng. 2020. Self-supervised graph representation learning via global context prediction. arXiv preprint arXiv:2003.01604 (2020)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3257759"},{"key":"e_1_3_2_1_29_1","volume-title":"Self-supervised graph transformer on large-scale molecular data. Advances in neural information processing systems","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020. Self-supervised graph transformer on large-scale molecular data. Advances in neural information processing systems, Vol. 33 (2020), 12559-12571."},{"key":"e_1_3_2_1_30_1","volume-title":"Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903","author":"Rong Yu","year":"2019","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903 (2019)."},{"key":"e_1_3_2_1_31_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."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.84.056111"},{"key":"e_1_3_2_1_33_1","volume-title":"Improved deep metric learning with multi-class n-pair loss objective. Advances in neural information processing systems","author":"Sohn Kihyuk","year":"2016","unstructured":"Kihyuk Sohn. 2016. Improved deep metric learning with multi-class n-pair loss objective. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_3_2_1_34_1","volume-title":"Mehdi Azabou, Eva L Dyer, Remi Munos and Michal Valko.","author":"Thakoor Shantanu","year":"2021","unstructured":"Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos and Michal Valko. 2021. Large-scale representation learning on graphs via bootstrapping. arXiv preprint arXiv:2102.06514 (2021)."},{"key":"e_1_3_2_1_35_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_36_1","volume-title":"Deep graph infomax. arXiv preprint arXiv:1809.10341","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2018. Deep graph infomax. arXiv preprint arXiv:1809.10341 (2018)."},{"key":"e_1_3_2_1_37_1","volume-title":"LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings. arXiv preprint arXiv:2408.14512","author":"Wang Duo","year":"2024","unstructured":"Duo Wang, Yuan Zuo, Fengzhi Li, and Junjie Wu. 2024c. LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings. arXiv preprint arXiv:2408.14512 (2024)."},{"key":"e_1_3_2_1_38_1","unstructured":"Lu Wang Chao Du Pu Zhao Chuan Luo Zhangchi Zhu Bo Qiao Wei Zhang Qingwei Lin Saravan Rajmohan Dongmei Zhang et al. 2024a. Contrastive Learning with Negative Sampling Correction. arXiv preprint arXiv:2401.08690 (2024)."},{"key":"e_1_3_2_1_39_1","volume-title":"International conference on machine learning. PMLR, 9929-9939","author":"Wang Tongzhou","year":"2020","unstructured":"Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International conference on machine learning. PMLR, 9929-9939."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467415"},{"key":"e_1_3_2_1_41_1","volume-title":"Machine learning in molecular sciences","author":"Wang Yuyang","unstructured":"Yuyang Wang, Zijie Li, and Amir Barati Farimani. 2023. Graph neural networks for molecules. In Machine learning in molecular sciences. Springer, 21-66."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657927"},{"key":"e_1_3_2_1_43_1","volume-title":"Self-supervised learning of graph neural networks: A unified review","author":"Xie Yaochen","year":"2022","unstructured":"Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji. 2022. Self-supervised learning of graph neural networks: A unified review. IEEE transactions on pattern analysis and machine intelligence, Vol. 45, 2 (2022), 2412-2429."},{"key":"e_1_3_2_1_44_1","volume-title":"Relative density-ratio estimation for robust distribution comparison. Neural computation","author":"Yamada Makoto","year":"2013","unstructured":"Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, and Masashi Sugiyama. 2013. Relative density-ratio estimation for robust distribution comparison. Neural computation, Vol. 25, 5 (2013), 1324-1370."},{"key":"e_1_3_2_1_45_1","volume-title":"International Conference on Machine Learning. PMLR, 12121-12132","author":"You Yuning","year":"2021","unstructured":"Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021. Graph contrastive learning automated. In International Conference on Machine Learning. PMLR, 12121-12132."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539425"},{"key":"e_1_3_2_1_47_1","first-page":"3434","article-title":"Graph Debiased Contrastive Learning with Joint Representation Clustering","author":"Zhao Han","year":"2021","unstructured":"Han Zhao, Xu Yang, Zhenru Wang, Erkun Yang, and Cheng Deng. 2021. Graph Debiased Contrastive Learning with Joint Representation Clustering.. In IJCAI. 3434-3440.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_48_1","volume-title":"Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"}],"event":{"name":"SIGIR '25: The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Padua Italy","acronym":"SIGIR '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3726302.3730007","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T12:20:46Z","timestamp":1755865246000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3726302.3730007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,13]]},"references-count":49,"alternative-id":["10.1145\/3726302.3730007","10.1145\/3726302"],"URL":"https:\/\/doi.org\/10.1145\/3726302.3730007","relation":{},"subject":[],"published":{"date-parts":[[2025,7,13]]},"assertion":[{"value":"2025-07-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}