{"type":"ScholarlyArticle","title":"Negative Metric Learning for Graphs","authors":[{"name":"Yiyang Zhao","slug":"yiyang-zhao","given":"Yiyang","family":"Zhao","affiliation":""},{"name":"Chengpei Wu","slug":"chengpei-wu","given":"Chengpei","family":"Wu","affiliation":""},{"name":"Lilin Zhang","slug":"lilin-zhang","given":"Lilin","family":"Zhang","affiliation":""},{"name":"Ning Yang","slug":"ning-yang","given":"Ning","family":"Yang","affiliation":""}],"abstract":"Graph contrastive learning (GCL) often suffers from false negatives, which degrades the  performance on downstream tasks. The existing methods addressing the false negative issue usually rely on human prior knowledge, still leading GCL to suboptimal results. In this paper, we propose a novel Negative Metric Learning (NML) enhanced GCL (NML-GCL). NML-GCL employs a learnable Negative Metric Network (NMN) to build a negative metric space, in which false negatives can be distinguished better from true negatives based on their distance to anchor node. To overcome the lack of explicit supervision signals for NML, we propose a joint training scheme with bi-level optimization objective, which implicitly utilizes the self-supervision signals to iteratively optimize the encoder and the negative metric network. The solid theoretical analysis and the extensive experiments conducted on widely used benchmarks verify the superiority of the proposed method.","doi":"10.24963/ijcai.2025/794","url":"https://ijcai.org/proceedings/2025/794","pdf":"https://ijcai.org/proceedings/2025/0794.pdf","markdown":"https://ijcai.org/proceedings/2025/794.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/794","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":794,"pages":{"first":7137,"last":7145},"keywords":["Machine Learning: ML: Sequence and graph learning","Machine Learning: ML: Self-supervised Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}