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Data"],"published-print":{"date-parts":[[2026,9,30]]},"abstract":"<jats:p>Label scarcity severely hinders the performance of the node classification task on graphs. To overcome the lack of supervised information provided by labels, some work has attempted to attach a pretext task based on self-supervised learning, aiming to mine intrinsic information from the graph data itself as a complement. 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},"page":"1-24","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","volume":"20","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, Shanghai, China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai, 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, Shanghai, China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai, 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, Guangzhou, 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, Shanghai, China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai, 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, Shanghai, China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1145\/3487553.3524722","volume-title":"Companion Proceedings of the Web Conference 2022","author":"Akkas Selahattin","year":"2022","unstructured":"Selahattin Akkas and Ariful Azad. 2022. 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GCC: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1150\u20131160."},{"key":"e_1_3_1_34_2","first-page":"5241","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Qu Meng","year":"2019","unstructured":"Meng Qu, Yoshua Bengio, and Jian Tang. 2019. GMNN: Graph Markov neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 5241\u20135250."},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1007\/s10994-023-06464-0","article-title":"Improving fraud detection via imbalanced graph structure learning","volume":"113","author":"Ren Lingfei","year":"2024","unstructured":"Lingfei Ren, Ruimin Hu, Yang Liu, Dengshi Li, Junhang Wu, Yilong Zang, and Wenyi Hu. 2024. Improving fraud detection via imbalanced graph structure learning. 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A modified GNN architecture with enhanced aggregator and message passing functions. Engineering Applications of Artificial Intelligence 122 (2023), 106077.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"e_1_3_1_38_2","doi-asserted-by":"crossref","first-page":"110882","DOI":"10.1016\/j.patcog.2024.110882","article-title":"Sparse feature selection using hypergraph Laplacian-based semi-supervised discriminant analysis","volume":"157","author":"Sheikhpour Razieh","year":"2025","unstructured":"Razieh Sheikhpour, Kamal Berahmand, Mehrnoush Mohammadi, and Hassan Khosravi. 2025. Sparse feature selection using hypergraph Laplacian-based semi-supervised discriminant analysis. Pattern Recognition 157 (2025), 110882.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"121800","DOI":"10.1016\/j.ins.2024.121800","article-title":"Robust semi-supervised multi-label feature selection based on shared subspace and manifold learning","volume":"699","author":"Sheikhpour Razieh","year":"2025","unstructured":"Razieh Sheikhpour, Mehrnoush Mohammadi, Kamal Berahmand, Farid Saberi-Movahed, and Hassan Khosravi. 2025. Robust semi-supervised multi-label feature selection based on shared subspace and manifold learning. Information Sciences 699 (2025), 121800.","journal-title":"Information Sciences"},{"issue":"11","key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"8174","DOI":"10.1109\/TNNLS.2022.3155478","article-title":"Graph-based semi-supervised learning: A comprehensive review","volume":"34","author":"Song Zixing","year":"2022","unstructured":"Zixing Song, Xiangli Yang, Zenglin Xu, and Irwin King. 2022. Graph-based semi-supervised learning: A comprehensive review. IEEE Transactions on Neural Networks and Learning Systems 34, 11 (2022), 8174\u20138194.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1609\/aaai.v36i4.20335","article-title":"Graph structure learning with variational information bottleneck","volume":"36","author":"Sun Qingyun","year":"2022","unstructured":"Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and Philip S. Yu. 2022. Graph structure learning with variational information bottleneck. Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022), 4165\u20134174.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_1_42_2","first-page":"787","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining","author":"Tan Qiaoyu","year":"2023","unstructured":"Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li, Rui Chen, and Xia Hu. 2023. S2GAE: Self-supervised graph autoencoders are generalizable learners with graph masking. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining, 787\u2013795."},{"key":"e_1_3_1_43_2","doi-asserted-by":"crossref","first-page":"407","DOI":"10.18653\/v1\/P19-2057","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop","author":"Thongtan Tan","year":"2019","unstructured":"Tan Thongtan and Tanasanee Phienthrakul. 2019. Sentiment classification using document embeddings trained with cosine similarity. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 407\u2013414."},{"key":"e_1_3_1_44_2","first-page":"1","volume-title":"Proceedings of the 2015 IEEE Information Theory Workshop (ITW)","author":"Tishby Naftali","year":"2015","unstructured":"Naftali Tishby and Noga Zaslavsky. 2015. Deep learning and the information bottleneck principle. In Proceedings of the 2015 IEEE Information Theory Workshop (ITW). IEEE, 1\u20135."},{"key":"e_1_3_1_45_2","unstructured":"Zekun Tong Yuxuan Liang Changsheng Sun David S. Rosenblum and Andrew Lim. 2020. Directed graph convolutional network. arXiv:2004.13970. Retrieved from https:\/\/arxiv.org\/abs\/2004.13970"},{"key":"e_1_3_1_46_2","first-page":"4","article-title":"Graph attention networks","volume":"1050","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. Stat 1050 (2018), 4.","journal-title":"Stat"},{"key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"10049","DOI":"10.1609\/aaai.v35i11.17206","article-title":"Contrastive and generative graph convolutional networks for graph-based semi-supervised learning","volume":"35","author":"Wan Sheng","year":"2021","unstructured":"Sheng Wan, Shirui Pan, Jian Yang, and Chen Gong. 2021. Contrastive and generative graph convolutional networks for graph-based semi-supervised learning. 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Information Sciences 307 (2015), 39\u201352.","journal-title":"Information Sciences"},{"issue":"2","key":"e_1_3_1_54_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3607144","article-title":"Open-world graph active learning for node classification","volume":"18","author":"Xu Hui","year":"2023","unstructured":"Hui Xu, Liyao Xiang, Junjie Ou, Yuting Weng, Xinbing Wang, and Chenghu Zhou. 2023. Open-world graph active learning for node classification. ACM Transactions on Knowledge Discovery from Data 18, 2 (2023), 1\u201320.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_55_2","first-page":"1119","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Xu Jie","year":"2023","unstructured":"Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen, Gang Niu, and Xiaofeng Zhu. 2023. Self-weighted contrastive learning among multiple views for mitigating representation degeneration. In Advances in Neural Information Processing Systems, Vol. 36, 1119\u20131131."},{"key":"e_1_3_1_56_2","first-page":"1","volume-title":"Proceedings of the 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)","author":"Yan Kuan","year":"2023","unstructured":"Kuan Yan, Junbin Gao, and Dmytro Matsypura. 2023. FIW-GNN: A heterogeneous graph-based learning model for credit card fraud detection. In Proceedings of the 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 1\u201310."},{"key":"e_1_3_1_57_2","first-page":"5812","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. In Advances in Neural Information Processing Systems, Vol. 33, 5812\u20135823."},{"key":"e_1_3_1_58_2","doi-asserted-by":"crossref","first-page":"16578","DOI":"10.1609\/aaai.v38i15.29596","article-title":"HGPROMPT: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning","volume":"38","author":"Yu Xingtong","year":"2024","unstructured":"Xingtong Yu, Yuan Fang, Zemin Liu, and Xinming Zhang. 2024. HGPROMPT: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning. 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SMGCL: Semi-supervised multi-view graph contrastive learning. Knowledge-Based Systems 260 (2023), 110120.","journal-title":"Knowledge-Based Systems"},{"issue":"8","key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.1109\/TKDE.2024.3363703","article-title":"Differentiable clustering for graph attention","volume":"36","author":"Zhou Haicang","year":"2024","unstructured":"Haicang Zhou, Tiantian He, Yew-Soon Ong, Gao Cong, and Quan Chen. 2024. Differentiable clustering for graph attention. IEEE Transactions on Knowledge and Data Engineering 36, 8 (2024), 3751\u20133764.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_65_2","first-page":"4917","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Zhou Kaixiong","year":"2020","unstructured":"Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu. 2020. Towards deeper graph neural networks with differentiable group normalization. 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