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A unified framework for convolution-based graph neural networks
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AI
DOI：10.1016/j.patcog.2024.110597.png)
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Abstract
En 中文
Graph Convolutional Networks (GCNs) have attracted a lot of research interest in machine learning, and many variants have been proposed recently. In this paper, we take a step forward to establish a unified framework for convolution -based graph neural networks, aiming to provide a systematic view of different GCN variants and deep understanding of the relations among them. Our key idea is formulating the basic graph convolution operation as an optimization problem in the graph Fourier space. Under this framework, a variety of popular GCN models, including vanilla-GCNs, attention -based GCNs and topology -based GCNs, can be interpreted as a similar optimization problem but with different regularizers. This novel perspective enables a better understanding of the similarities and differences among many widely used GCNs, and may inspire new model designs. As a showcase, we present a novel regularization technique under the proposed framework to tackle the oversmoothing problem in graph convolution. The effectiveness of newly designed model is validated empirically.
Keywords:
Laplacian optimization
Graph convolution network
Graph neural networks
Graph Fourier space
Oversmoothing