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Unsupervised social network embedding via adaptive specific mappings

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Abstract

In this paper, we address the problem of unsuperised social network embedding, which aims to embed network nodes, including node attributes, into a latent low dimensional space. In recent methods, the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction performance. However, the non-linear property of node attributes and network structure is not efficiently fused in existing methods, which is potentially helpful in learning a better network embedding. To this end, in this paper, we propose a novel model called ASM (Adaptive Specific Mapping) based on encoder-decoder framework. In encoder, we use the kernel mapping to capture the non-linear property of both node attributes and network structure. In particular, we adopt two feature mapping functions, namely an untrainable function for node attributes and a trainable function for network structure. By the mapping functions, we obtain the low dimensional feature vectors for node attributes and network structure, respectively. Then, we design an attention layer to combine the learning of both feature vectors and adaptively learn the node embedding. In encoder, we adopt the component of reconstruction for the training process of learning node attributes and network structure. We conducted a set of experiments on seven real-world social network datasets. The experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

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Acknowledgements

The authors would like to thank sincerely the anonymous editors and reviewers for their helpful comments and suggestions. The research was supported by the National Natural Science Foundation of China (Grant Nos. 61572537, U1501252).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510275, China

    Youming Ge, Cong Huang, Yubao Liu, Sen Zhang & Weiyang Kong

  2. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China

    Yubao Liu

Authors
  1. Youming Ge
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  2. Cong Huang
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  3. Yubao Liu
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  4. Sen Zhang
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  5. Weiyang Kong
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Corresponding author

Correspondence to Yubao Liu.

Additional information

Youming Ge is currently working towards the PhD degree in the School of Computer Science and Engineering, Sun Yat-Sen University, China. His research interests include data mining and artificial intelligence.

Cong Huang is graduate student of the School of Computer Science and Engineering, Sun Yat-Sen University, China. His research interests include data mining and artificial intelligence.

Yubao Liu is currently a professor with the Department of Computer Science of Sun Yat-Sen University, China. He received his PhD in computer science from Huazhong University of Science and Technology, China in 2003. He has published more than 50 refereed journal and conference papers including SIGMOD, TODS, VLDB, and VLDBJ, etc. His research interests include database systems and data mining. He is a senior member of the China Computer Federation (CCF).

Sen Zhang is currently working towards the PhD degree in the School of Computer Science and Engineering, Sun Yat-Sen University, China. His research interests include data mining and artificial intelligence.

Weiyang Kong is currently working towards the PhD degree in the School of Computer Science and Engineering, Sun Yat-Sen University, China. His research interests include data mining and artificial intelligence.

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Ge, Y., Huang, C., Liu, Y. et al. Unsupervised social network embedding via adaptive specific mappings. Front. Comput. Sci. 18, 183310 (2024). https://doi.org/10.1007/s11704-023-2180-3

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  • DOI: https://doi.org/10.1007/s11704-023-2180-3

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