[1]ZHENG Wenping,SU Rui,LIU Yang.Few-shot node classification based on task-aware subgraphs[J].CAAI Transactions on Intelligent Systems,2026,21(3):666-674.[doi:10.11992/tis.202506037]
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Few-shot node classification based on task-aware subgraphs

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 3 Page number: 666-674 Column: 学术论文—机器学习 Public date: 2026-05-05
Title:
Few-shot node classification based on task-aware subgraphs
Author(s):
ZHENG Wenping1; 2; 3;  SU Rui1;  LIU Yang1
1. College of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Computational Intelligence and Chinese Information Processing, Ministry of Education (Shanxi University), Taiyuan 030006, China;
3. Institute of Intelligent Information Processing, Shanxi University, Taiyuan 030006, China
Keywords:
graph neural networks; node classification; graph representation learning; few shot learning; meta learning; complex network; prototypical network; deep learning
CLC:
TP30
DOI:
10.11992/tis.202506037
Abstract:
Current graph neural network-based node classification methods rely on large amounts of labeled data and are limited by long-tailed label distributions. In low-resource settings, meta-learning-based few-shot learning is effective for graph representation. However, global graph learning introduces task-irrelevant noise, and current prototypical networks, which classify nodes by directly computing similarities between nodes and class prototypes, struggle to estimate accurate prototypes under few-shot conditions. To address these issues, we propose a task-aware subgraph-based few-shot node classification method (TAS-FNC). This method constructs high-connectivity subgraphs for each task through structural pruning and topological enhancement, enabling task-specific node representation learning and reducing noise. It then models the relationships between query nodes and class prototypes for classification. Experiments on four datasets against 11 baselines show that TAS-FNC effectively improves node classification accuracy in label-scarce scenarios.
References:
[1] WANG Weiqing, YIN Hongzhi, DU Xingzhong, et al. Online user representation learning across heterogeneous social networks[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2019: 545-554.
[2] FOUT A, BYRD J, SHARIAT B, et al. Protein interface prediction using graph convolutional networks[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017: 6533-6542.
[3] 于浩淼, 刘炜, 孟流畅, 等. RWK-GNN: 基于特征增强与子核分解的非平衡图欺诈检测算法[J]. 电子学报, 2024, 52(10): 3382-3391 YU Haomiao, LIU Wei, MENG Liuchang, et al. RWK-GNN: fraud detection for imbalanced graphs with feature enhancement and subkernel decomposition[J]. Acta electronica sinica, 2024, 52(10): 3382-3391
[4] 刘晓光, 李梅. 基于深度学习的药物-靶标相互作用预测研究综述[J]. 智能系统学报, 2024, 19(3): 494-524 LIU Xiaoguang, LI Mei. A survey of deep learning-based drug-target interaction prediction[J]. CAAI transactions on intelligent systems, 2024, 19(3): 494-524
[5] DING Kaize, WANG Jianling, CAVERLEE J, et al. Meta propagation networks for graph few-shot semi-supervised learning[J]. Proceedings of the AAAI conference on artificial intelligence, 2022, 36(6): 6524-6531
[6] DONG Chuanqi, LI Wenbin, HUO Jing, et al. Learning task-aware local representations for few-shot learning[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. New York: ACM, 2021: 716-722.
[7] WANG Ning, LUO Minnan, DING Kaize, et al. Graph few-shot learning with attribute matching[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York: ACM, 2020: 1545-1554.
[8] WANG Song, CHEN Chen, LI Jundong. Graph few-shot learning with task-specific structures[C]//Proceedings of the 36th International Conference on Neural Information Processing Systems. New York: ACM, 2022: 38925-38936.
[9] LIU Zemin, FANG Yuan, LIU Chenghao, et al. Relative and absolute location embedding for few-shot node classification on graph[J]. Proceedings of the AAAI conference on artificial intelligence, 2021, 35(5): 4267-4275
[10] ZHOU Fan, CAO Chengtai, ZHANG Kunpeng, et al. Meta-GNN: on few-shot node classification in graph meta-learning[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM, 2019: 2357-2360.
[11] WU F, ZHANG Tianyi, DE SOUZA A H, et al. Simplifying graph convolutional networks[C]//International Conference on Machine Learning. Long Beach: PMLR, 2019: 6861-6871.
[12] VINYALS O, BLUNDELL C, LILLICRAP T, et al. Matching networks for one shot learning[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. New York: ACM, 2016: 3637-3645.
[13] YU Xingtong, FANG Yuan, LIU Zemin, et al. A survey of few-shot learning on graphs: from meta-learning to pre-training and prompt learning[EB/OL]. (2024-03-15)[2025-12-22]. https://arxiv.org/abs/2402.01440.
[14] 刘美麟, 李华, 郑文萍. 模体感知的多视图协同聚类优化算法[J]. 计算机研究与发展, 2026, 63(2): 324-337 LIU Meilin, LI Hua, ZHENG Wenping. Motif-aware multi-view cooperative clustering optimization algorithm[J]. Journal of computer research and development, 2026, 63(2): 324-337
[15] 郑文萍, 王英楠, 杨贵. 基于双监督网络嵌入的社区发现算法[J]. 模式识别与人工智能, 2022, 35(3): 283-290 ZHENG Wenping, WANG Yingnan, YANG Gui. Dual supervised network embedding based community detection algorithm[J]. Pattern recognition and artificial intelligence, 2022, 35(3): 283-290
[16] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2016-09-09)[2025-12-22]. https://arxiv.org/abs/1609.02907.
[17] HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017: 1025-1035.
[18] VELI?KOVI? P, CUCURULL G, CASANOVA A, et al. Graph attention networks[C]//Proceedings of the 6th International Conference on Learning Representations. Vancouver: ICLR, 2018: 1-12.
[19] 周凯锐, 刘鑫, 景丽萍, 等. 概念驱动的小样本判别特征学习方法[J]. 智能系统学报, 2023, 18(1): 162-172 ZHOU Kairui, LIU Xin, JING Liping, et al. Concept-driven discriminative feature learning for few-shot learning[J]. CAAI transactions on intelligent systems, 2023, 18(1): 162-172
[20] GHAROUN H, MOMENIFAR F, CHEN Fang, et al. Meta-learning approaches for few-shot learning: a survey of recent advances[J]. ACM computing surveys, 2024, 56(12): 1-41
[21] SNELL J, SWERSKY K, ZEMEL R S. Prototypical networks for few-shot learning[C]//Neural Information Processing Systems. Red Hook: Curran Associates, 2017: 4077-4087.
[22] FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//Proceedings of the 34th International Conference on Machine Learning-Volume 70. Sydney: PMLR, 2017: 1126-1135.
[23] WANG Song, DING Kaize, ZHANG Chuxu, et al. Task-adaptive few-shot node classification[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM, 2022: 1910-1919.
[24] WANG Song, DONG Yushun, DING Kaize, et al. Few-shot node classification with extremely weak supervision[C]//Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. New York: ACM, 2023: 276-284.
[25] DING Kaize, WANG Jianling, LI Jundong, et al. Graph prototypical networks for few-shot learning on attributed networks[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York: ACM, 2020: 295-304.
[26] LAN L, WANG P, DU X, et al. Node classification on graphs with few-shot novel labels via meta transformed network embedding[J]. Advances in Neural Information Processing Systems, 2020, 33: 16520-16531
[27] WANG Ningrui, LAI Yujing, CHEN Chuan, et al. Prototypical networks with unlabeled data for few-shot node classification[J]. Engineering applications of artificial intelligence, 2025, 145: 110088
[28] 刘鑫磊, 冯林, 廖凌湘, 等. 基于元学习的图卷积网络少样本学习模型[J]. 电子学报, 2024, 52(3): 885-897 LIU Xinlei, FENG Lin, LIAO Lingxiang, et al. Few-shot learning on graph convolutional network based on meta learning[J]. Acta electronica sinica, 2024, 52(3): 885-897
[29] MCAULEY J, PANDEY R, LESKOVEC J. Inferring networks of substitutable and complementary products[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2015: 785-794.
[30] TANG Jie, ZHANG Jing, YAO Limin, et al. ArnetMiner: extraction and mining of academic social networks[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2008: 990-998.
[31] HU W, FEY M, ZITNIK M, et al. Open graph benchmark: datasets for machine learning on graphs[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2020: 1-12.
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