Abstract
Abstract
Semi-supervised node classification is an important task that aims at classifying nodes based on the graph structure, node features, and class labels for a subset of nodes. While most graph convolutional networks (GCNs) perform well when an ample number of labeled nodes are available, they often degenerate when the amount of labeled data is limited. To address this problem, we propose a scheme, namely, Individuality-enhanced and Multi-granularity Consistency-preserving graph neural Network (IMCN), which can alleviate the problem of losing individual information within the encoder while providing a reliable supervised signal for learning purposes. First, one simple encoder based on node features only is integrated to enhance node individuality and amend node commonality learned by the GCN-based encoder. Then, three constraints are defined at different levels of granularity, encompassing node embedding agreement, semantic class alignment, and node-to-class distribution identity. They can maintain the consistency between the individuality and commonality of nodes and be leveraged as latent supervised signals for learning representative embeddings. Finally, the trade-off between the individuality and commonality of nodes captured by two encoders is taken into consideration for node classification. Extensive experiments on six real-world datasets have been conducted to validate the superiority of IMCN against state-of-the-art baselines in handling node classification tasks with scarce labeled data.
Graphical abstract
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Reference47 articles.
1. Wang K, An J, Zhou M, Shi Z, Shi X, Kang Q (2023) Minority-weighted graph neural network for imbalanced node classification in social networks of internet of people. IEEE Internet Things J 10(1):330–340
2. Yu H, Shen Z, Du P (2022) NPI-RGCNAE: fast predicting ncRNA-protein interactions using the relational graph convolutional network autoencoder. IEEE J Biomed Health Inform 26(4):1861–1871
3. Zheng Y, Gao C, He X, Jin D, Li Y (2023) Incorporating price into recommendation with graph convolutional networks. IEEE Trans Knowl Data Eng 35(2):1609–1623
4. Liu J, Xia F, Feng X, Ren J, Liu H (2022) Deep graph learning for anomalous citation detection. IEEE Trans Neural Netw Learn Syst 33(6):2543–2557
5. Lin G, Kang X, Liao K, Zhao F, Chen Y (2021) Deep graph learning for semi-supervised classification. Pattern Recognit 118:108039
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