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However, the representation learning capacity of existing methods is undermined by two main factors: 1) insufficient supervised information caused by the underutilization of limited labels, and 2) the multi-view expressiveness capabilities of intrinsic information for an individual model are restricted. Therefore, we propose an information-enhanced method for label scarcity on graphs (GNN-LS). For supervised information, we develop a dual guidance strategy that effectively utilizes unlabeled nodes while considering prediction reliability to provide stronger extra supervised guidance for the model. Meanwhile, considering the possible redundancy of the model in acquiring two types of information, we construct a regularization to reduce it. Finally, to effectively expand the range of views on the intrinsic information mined, we design a multi-view integration mechanism, which determines coefficients between intrinsic information from different views according to their respective fitness for the current dataset and label rate. Comprehensive experiments show that our method achieves significant performance gains compared with its peers on seven benchmark datasets.<\/jats:p>","DOI":"10.1145\/3837756","type":"journal-article","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T14:49:41Z","timestamp":1787410181000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["GNN-LS: An Information-Enhanced Method for Label Scarcity on Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7612-2122","authenticated-orcid":false,"given":"Haoran","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7185-9731","authenticated-orcid":false,"given":"Junli","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6280-0319","authenticated-orcid":false,"given":"Rui","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Cyber Engineering, Guangzhou University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9987-0623","authenticated-orcid":false,"given":"Xin","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1917-9616","authenticated-orcid":false,"given":"Chungang","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,22]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"JGCL: Joint Self-Supervised and Supervised Graph Contrastive Learning. 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