{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T23:51:08Z","timestamp":1785887468156,"version":"3.56.0"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62192783"],"award-info":[{"award-number":["62192783"]}],"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":["62376117"],"award-info":[{"award-number":["62376117"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008048","name":"Nanjing University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008048","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["23BJL035"],"award-info":[{"award-number":["23BJL035"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.ins.2026.123714","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:13:05Z","timestamp":1780585985000},"page":"123714","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["HALF: A homophily-aware loss fusion for robust learning under label noise in heterophilic graphs"],"prefix":"10.1016","volume":"754","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6667-8330","authenticated-orcid":false,"given":"Shuangjie","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1008-5509","authenticated-orcid":false,"given":"Meng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqing","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1210-9723","authenticated-orcid":false,"given":"Xinyu","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2026.123714_bib0005","series-title":"The World Wide Web Conference","first-page":"3448","article-title":"Your style your identity: leveraging writing and photography styles for drug trafficker identification in darknet markets over attributed heterogeneous information network","author":"Zhang","year":"2019"},{"issue":"5","key":"10.1016\/j.ins.2026.123714_bib0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3535101","article-title":"Graph neural networks in recommender systems: a survey","volume":"55","author":"Wu","year":"2022","journal-title":"ACM Comput. Surv."},{"issue":"4","key":"10.1016\/j.ins.2026.123714_bib0015","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1111\/j.1539-6924.2006.00791.x","article-title":"Using graph models to analyze the vulnerability of electric power networks","volume":"26","author":"Holmgren","year":"2006","journal-title":"Risk Anal."},{"issue":"1","key":"10.1016\/j.ins.2026.123714_bib0020","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.ins.2026.123714_bib0025","series-title":"International Conference on Machine Learning","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"key":"10.1016\/j.ins.2026.123714_bib0030","series-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017, Vol. 70 of Proceedings of Machine Learning Research","first-page":"233","article-title":"A closer look at memorization in deep networks","author":"Arpit","year":"2017"},{"key":"10.1016\/j.ins.2026.123714_bib0035","series-title":"Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024","article-title":"NoisyGL: a comprehensive benchmark for graph neural networks under label noise","author":"Wang","year":"2024"},{"key":"10.1016\/j.ins.2026.123714_bib0040","series-title":"8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020","article-title":"DivideMix: learning with noisy labels as semi-supervised learning","author":"Li","year":"2020"},{"key":"10.1016\/j.ins.2026.123714_bib0045","series-title":"International Conference on Machine Learning","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","author":"Yu","year":"2019"},{"key":"10.1016\/j.ins.2026.123714_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107338","article-title":"Graph neural networks with coarse-and fine-grained division for mitigating label noise and sparsity","volume":"187","author":"Li","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.ins.2026.123714_bib0055","series-title":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","first-page":"607","article-title":"Robust training of graph neural networks via noise governance","author":"Qian","year":"2023"},{"key":"10.1016\/j.ins.2026.123714_bib0060","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"433","article-title":"Resurrecting label propagation for graphs with heterophily and label noise","author":"Cheng","year":"2024"},{"key":"10.1016\/j.ins.2026.123714_bib0065","series-title":"5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.ins.2026.123714_bib0070","first-page":"7793","article-title":"Beyond homophily in graph neural networks: current limitations and effective designs","volume":"33","author":"Zhu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123714_bib0075","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5192","article-title":"Jo-src: a contrastive approach for combating noisy labels","author":"Yao","year":"2021"},{"key":"10.1016\/j.ins.2026.123714_bib0080","series-title":"International Conference on Machine Learning","first-page":"24851","article-title":"Investigating why contrastive learning benefits robustness against label noise","author":"Xue","year":"2022"},{"key":"10.1016\/j.ins.2026.123714_bib0085","first-page":"10111","article-title":"Learning from noisy labels with complementary loss functions","author":"Wang","year":"2021"},{"key":"10.1016\/j.ins.2026.123714_bib0090","series-title":"Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence","first-page":"1919","article-title":"Robust loss functions under label noise for deep neural networks","author":"Ghosh","year":"2017"},{"key":"10.1016\/j.ins.2026.123714_bib0095","series-title":"European Conference on Computer Vision","first-page":"516","article-title":"Self-filtering: a noise-aware sample selection for label noise with confidence penalization","author":"Wei","year":"2022"},{"key":"10.1016\/j.ins.2026.123714_bib0100","article-title":"Co-teaching: robust training of deep neural networks with extremely noisy labels","volume":"31","author":"Han","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123714_bib0105","series-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","first-page":"227","article-title":"Nrgnn: learning a label noise resistant graph neural network on sparsely and noisily labeled graphs","author":"Dai","year":"2021"},{"key":"10.1016\/j.ins.2026.123714_bib0110","author":"Hoang"},{"key":"10.1016\/j.ins.2026.123714_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106113","article-title":"Contrastive learning of graphs under label noise","volume":"172","author":"Li","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.ins.2026.123714_bib0120","series-title":"5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings","article-title":"The concrete distribution: a continuous relaxation of discrete random variables","author":"Maddison","year":"2017"},{"key":"10.1016\/j.ins.2026.123714_bib0125","series-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","first-page":"392","article-title":"Adagnn: graph neural networks with adaptive frequency response filter","author":"Dong","year":"2021"},{"key":"10.1016\/j.ins.2026.123714_bib0130","series-title":"International Conference on Machine Learning","first-page":"13242","article-title":"Finding global homophily in graph neural networks when meeting heterophily","author":"Li","year":"2022"},{"key":"10.1016\/j.ins.2026.123714_bib0135","series-title":"Workshop on Challenges in Representation Learning, ICML, 3","first-page":"896","article-title":"Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks","author":"Lee","year":"2013"},{"key":"10.1016\/j.ins.2026.123714_bib0140","first-page":"596","article-title":"Fixmatch: simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123714_bib0145","series-title":"Forty-First International Conference on Machine Learning","article-title":"S3GCL: spectral, swift, spatial graph contrastive learning","author":"Wan","year":"2024"},{"key":"10.1016\/j.ins.2026.123714_bib0150","series-title":"Proceedings of the ACM Web Conference 2022","first-page":"1392","article-title":"Towards unsupervised deep graph structure learning","author":"Liu","year":"2022"},{"key":"10.1016\/j.ins.2026.123714_bib0155","first-page":"22118","article-title":"Open graph benchmark: datasets for machine learning on graphs","volume":"33","author":"Hu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123714_bib0160","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1944","article-title":"Making deep neural networks robust to label noise: a loss correction approach","author":"Patrini","year":"2017"},{"key":"10.1016\/j.ins.2026.123714_bib0165","series-title":"International Conference on Learning Representations","article-title":"Training deep neural-networks using a noise adaptation layer","author":"Goldberger","year":"2017"},{"key":"10.1016\/j.ins.2026.123714_bib0170","series-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8\u201314, 2019, Vancouver, BC, Canada","first-page":"8024","article-title":"PyTorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.ins.2026.123714_bib0175","author":"Fey"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526006456?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526006456?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T23:16:59Z","timestamp":1785885419000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526006456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":35,"alternative-id":["S0020025526006456"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123714","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"HALF: A homophily-aware loss fusion for robust learning under label noise in heterophilic graphs","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123714","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123714"}}