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HD-GCN:A Hybrid Diffusion Graph Convolutional Network by Yang, Zhi; Li, Kang; Gan, Haitao; Huang, Zhongwei; Shi, Ming is a scholarly article available to read on EtoBox.

What is HD-GCN:A Hybrid Diffusion Graph Convolutional Network about?

The information diffusion performance of GCN and its variant models is limited by the adjacency matrix, which can lower their performance. Therefore, we introduce a new framework for graph convolutional networks called Hybrid Diffusion-based Graph Convolutional Network (HD-GCN) to address the limitations of information diffusion caused by the adjacency matrix. In the HD-GCN framework, we initially utilize diffusion maps to facilitate the diffusion of information among nodes that are adjacent to each other in the feature space. This allows for the diffusion of information between similar points that may not have an adjacent relationship. Next, we utilize graph convolution to further propagate information among adjacent nodes after the diffusion maps, thereby enabling the spread of information among similar nodes that are adjacent in the graph. Finally, we employ the diffusion distances obtained through the use of diffusion maps to regularize and constrain the predicted labels of training nodes. This regularization method is then applied to the HD-GCN training, resulting in a smoother classification surface. The model proposed in this paper effectively overcomes the limitations of in

Author

Yang, Zhi; Li, Kang; Gan, Haitao; Huang, Zhongwei; Shi, Ming

Published

2023

Language

EN