Multi-scale fusion graph convolutional networks

Zhi Kong

doi:10.1016/j.eswa.2025.129815

Multi-scale fusion graph convolutional networks

2025-09-23

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DOI：10.1016/j.eswa.2025.129815

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Abstract

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• Multi-scale Fusion GCN integrates shallow and deep features to improve classification performance. • Graph structure optimization leverages an autoencoder to refine the graph topology and enhance feature learning. • Enhanced classification accuracy and robustness are demonstrated across multiple datasets.

Multi-scale fusion graph convolutional networks

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