Machine learning
图卷积网络 (GCN)
图卷积网络 (GCN) 是图结构数据的基础深度学习架构，由 Thomas N. Kipf 和 Max Welling 于 2017 年在 ICLR 上提出。它通过一阶谱近似将卷积运算扩展到不规则图域，使每个节点能够聚合其邻居的特征信息。该模型成为半监督节点分类的典型基线，并开启了现代图神经网络的研究议程。
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来源
- Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France. link ↗
- Hamilton, W. L. (2020). Graph Representation Learning. Morgan & Claypool (Synthesis Lectures on Artificial Intelligence and Machine Learning). ISBN: 978-1-68173-963-2
如何引用本页
ScholarGate. (2026, June 3). Graph Convolutional Network (Spectral GCN for Semi-Supervised Node Classification). ScholarGate. https://scholargate.app/zh/deep-learning/graph-convolutional-network
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