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Adversarial network embedding using structural similarity

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Abstract

Network embedding which aims to embed a given network into a low-dimensional vector space has been proved effective in various network analysis and mining tasks such as node classification, link prediction and network visualization. The emerging network embedding methods have shifted of emphasis in utilizing mature deep learning models. The neural-network based network embedding has become a mainstream solution because of its high efficiency and capability of preserving the nonlinear characteristics of the network. In this paper, we propose Adversarial Network Embedding using Structural Similarity (ANESS), a novel, versatile, low-complexity GAN-based network embedding model which utilizes the inherent vertex-to-vertex structural similarity attribute of the network. ANESS learns robustness and effective vertex embeddings via a adversarial training procedure. Specifically, our method aims to exploit the strengths of generative adversarial networks in generating high-quality samples and utilize the structural similarity identity of vertexes to learn the latent representations of a network. Meanwhile, ANESS can dynamically update the strategy of generating samples during each training iteration. The extensive experiments have been conducted on the several benchmark network datasets, and empirical results demonstrate that ANESS significantly outperforms other state-of-the-art network embedding methods.

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Acknowledgements

This work was supported by the National Key R&D Program of China (2018YFB1003404), the National Natural Science Foundation of China (Giant Nos. 61872070, U1811261), the Fundamental Research Funds for the Central Universities (N171605001) and Liao Ning Revitalization Talents Program (XLYC1807158).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Zihan Zhou, Yu Gu & Ge Yu

Authors
  1. Zihan Zhou
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  2. Yu Gu
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  3. Ge Yu
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Corresponding author

Correspondence to Yu Gu.

Additional information

Zihan Zhou received the Master degree from Northeastern University, China in 2020. His research interest is network embedding.

Yu Gu received the PhD degree in computer software and theory from Northeastern University, China in 2010. Currently, he is a professor and the PhD supervisor at Northeastern University, China. His current research interests include big data analysis, spatial data management and graph data management. He is a senior member of the China Computer Federation (CCF).

Ge Yu received the PhD degree in computer science from Kyushu University, Japan in 1996. He is currently a professor and the PhD supervisor at Northeastern University, China. His research interests include distributed and parallel database, OLAP and data warehousing, data integration, graph data management, etc. He is a member of ACM, a senior member of IEEE, and a Fellow of the China Computer Federation (CCF).

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Zhou, Z., Gu, Y. & Yu, G. Adversarial network embedding using structural similarity. Front. Comput. Sci. 15, 151603 (2021). https://doi.org/10.1007/s11704-020-9182-1

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  • DOI: https://doi.org/10.1007/s11704-020-9182-1

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