{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T04:04:56Z","timestamp":1780805096222,"version":"3.54.1"},"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":[[2025,9]]},"abstract":"<jats:p>Graph contrastive learning (GCL) enhances the self-supervised learning capacity for graph representation learning. Nevertheless, the previous research has neglected to consider one fundamental nature of GCL -- graph contrastive learning operates as a one-shot learner, guided by the widely utilized noise contrastive estimation (e.g., the InfoNCE loss). Theoretically, to initially investigate the factors that contribute to the one-shot learner essence, we analyze the InfoNCE-based objective and derive its equivalent form of the softmax-based cross-entropy function. It is concluded that the InfoNCE-based GCL is determined to be a (2n-1)-way 1-shot classifier (n is the number of nodes). In this particular context, each sample is indicative of a unique ideational class, and each class has only one sample. Consequently, the one-shot learning nature of GCL leads to the issue of the limited self-supervised signal. To further address the above issue, we propose a One-Shot Learner in Graph Contrastive Learning (OS-GCL). Firstly, we estimate the potential probability distributions of the deterministic node features and discrete graph topology. Secondly, we develop a probabilistic message-passing mechanism to propagate probability (of feature) on probability (of topology). Thirdly, we propose the ProbNCE loss functions to contrast distributions. Extensive experimental results demonstrate the superiority of OS-GCL. To the best of our knowledge, this is the first study to examine the one-shot learning essence and the limited self-supervised signal issue of GCL.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/330","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"2964-2972","source":"Crossref","is-referenced-by-count":1,"title":["OS-GCL: A One-Shot Learner in Graph Contrastive Learning"],"prefix":"10.24963","author":[{"given":"Cheng","family":"Ji","sequence":"first","affiliation":[{"name":"SKLCCSE, School of Computer Science and Engineering, Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenrui","family":"He","sequence":"additional","affiliation":[{"name":"SKLCCSE, School of Computer Science and Engineering, Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyun","family":"Sun","sequence":"additional","affiliation":[{"name":"SKLCCSE, School of Computer Science and Engineering, Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingcheng","family":"Fu","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"SKLCCSE, School of Computer Science and Engineering, Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:44Z","timestamp":1758627224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/330"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/330","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}