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Link to original content: https://doi.org/10.1007/s10489-023-04961-2
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Adversarial variational autoencoder for attributed graph embedding with high-frequency noise filtering

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Abstract

Attributed graph embedding aims to learn low-dimensional representations by fully exploiting both topological structure and rich attributes. The embeddings contain more useful information, thus yielding better performance in subsequent analysis tasks. Therefore, attributed network embedding has become an important research direction in recent years. However, most existing algorithms ignore the high-frequency noise contained in the features and do not normalize the embedding, which limits their performance in downstream tasks. To solve the above problem, we design an Adversarial Attributed Graph Embedding algorithm (AAGE) in conjunction with a Laplacian smoothing filter. In particular, the AAGE employs a variational autoencoder (VAE) to learn the embedding of nodes and attributes. Unlike most variational autoencoder-based algorithms, we first apply a carefully-designed Laplacian filter to smooth the features before performing autoencoder, secondly normalize the obtained embeddings of nodes and attributes during the autoencoder process, and finally devise a generative adversarial training method to enhance the robustness of the learned representations. To verify the potential of the proposed AAGE, we evaluate its performance on the tasks of node classification and node clustering on three real-world attributed networks. The results show that AAGE significantly outperforms state-of-the-art methods.

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Data Availability

Datasets derived from public resources and made available with the article: Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations (2017).

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant No. 62072288, 61702306, 61433012), the Taishan Scholar Program of Shandong Province, Shandong Youth Innovation Team, the Natural Science Foundation of Shandong Province (Grant No. ZR2018BF013, ZR2022MF268), and the Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province (Grant No. CICIP2020001).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Hui Zhou, Xin Liu, Xiangju Li, Zhongying Zhao & Chao Li

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  1. Hui Zhou
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Correspondence to Zhongying Zhao.

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Zhou, H., Liu, X., Li, X. et al. Adversarial variational autoencoder for attributed graph embedding with high-frequency noise filtering. Appl Intell 53, 26750–26762 (2023). https://doi.org/10.1007/s10489-023-04961-2

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