Markov-GWNN: Markov graph wavelet neural network for semi-supervised node classification

Binfeng Huang; Fulan Qian

doi:10.1117/12.2668163

20 April 2023 Markov-GWNN: Markov graph wavelet neural network for semi-supervised node classification

Binfeng Huang, Fulan Qian

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126023I (2023) https://doi.org/10.1117/12.2668163
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Graph wavelet neural network, as a popular method of graph neural networks, has achieved good result in representation learning. However, graph wavelet neural network faces the phenomenon of over-smoothing, which means that repeated propagation of multi-layer network makes node feature representation indistinguishable. The current methods to alleviate over-smoothing phenomenon of graph wavelet neural network have the problem of add more model parameters, which leads to running time become higher and training process more difficult. In order to solve the above problem, this paper proposes a new method called Markov graph wavelet neural network. On the one hand, Markov graph wavelet neural network decouples feature transformation and feature propagation. First, multi-layer perceptron is used for feature transformation, which reduces the number of model training parameters and reduces running time. On the other hand, Markov diffusion kernel is applied to k-order feature propagation, effectively obtains node local information and node global information. Besides, the influence of initial feature is considered to update target node representation, which effectively alleviates over-smoothing phenomenon. Finally, the experimental result of Markov graph wavelet neural network model on Cora, CiteSeer, PubMed exceeds the representative baseline algorithms, which fully demonstrates the superiority of Markov graph wavelet neural network.

Citation Download Citation

Binfeng Huang and Fulan Qian "Markov-GWNN: Markov graph wavelet neural network for semi-supervised node classification", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126023I (20 April 2023); https://doi.org/10.1117/12.2668163

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