GNN谱视角下的跨网络节点分类

doi:10.12066/j.issn.1007-2861.2517

Abstract

Abstract: To solve the small-sample problem, this paper aimed to analyze the mechanism of spectral information transmission across network nodes and established a spectral method for cross-network node classification based on domain adaptation. In theory, this paper proposed an intermediate-frequency graph convolutional kernel to introduce intermediate-frequency information for a graph neural network (GNN) and then analyzed the properties of different frequency information. The results showed that unlike low-frequency and high-frequency information, intermediate-frequency information preserved similar features of nodes both in a single network and across networks. It was demonstrated that intermediate-frequency information was considered more suitable for cross-network node classification than low- frequency and high-frequency information. In practice, an intermediate frequency-domain adaptive graph convolutional network (IFDA-GCN) was proposed for classifying nodes across networks. IFDA-GCN relied on the intermediate-frequency graph convolutional kernel to extract intermediate-frequency information from the source and target networks, while leveraging domain adaptation techniques to mitigate the distributional shift between networks. Experimental results on real-world datasets demonstrated that IFDA-GCN outperformed baselines, and that intermediate-frequency information outperformed low-frequency and high-frequency information in cross-network node classification.

Key words: graph neural network (GNN), frequency information, domain adaptation, node classification

CLC Number:

TP391

ZHU Zhaodi, PENG Yaxin. Cross-network node classification from the spectral perspective of GNN[J]. Journal of Shanghai University（Natural Science Edition）, 2026, 32(2): 295-311.

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Cross-network node classification from the spectral perspective of GNN

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