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Triple-layer attention mechanism-based network embedding approach for anchor link identification across social networks

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Abstract

Anchor link identification is a task that determines which pair of accounts in different social networks belongs to the same user. As a foundation of many applications, such as information dissemination across networks, anchor link identification has attracted much attention in recent years. Several methods learned a latent common space to preserve the structural proximity of accounts, such that the contributions of diverse neighbors are ignored due to the unweighted edges. In sparse networks, the overlapping of neighbors is rare and structural similarities are small, resulting in performance degradation of these methods. In this paper, we propose a triple-layer attention mechanism-based network embedding (TANE) method which utilizes the network structure to identify anchor links. TANE has two learning modules: the attention learning module which learns contribution weights of intranetwork and internetwork neighbors in anchor link identification under the supervision of observed anchor links, and the embedding learning module which tries to learn a latent common space by preserving weighted structural proximity of neighbors and second-order neighbors (i.e., neighbors of neighbors). By exploiting second-order neighbors, more structural information is introduced into embedding processing, which reduces sparsity. The weights and embeddings are transferred between modules and learned uniformly by Adam algorithm. Extensive experimental results of two real-world datasets show that TANE can improve the top-k hit ratio of anchor link identification compared with several state-of-the-art methods.

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Notes

  1. https://github.com/ColaLL/ABNE.

  2. The details of proving that these parameters are small numbers can be seen in Sects. 5.3, 5.4, and 5.5 and papers [7, 8, 11].

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Acknowledgements

The work has been supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61472069, 61402089, 61332006 and U1401256; the Fundamental Research Funds for the Central Universities under Grant No. N161602003 and China Scholarship Council.

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Authors and Affiliations

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

    Yao Li, Huiyuan Cui & Huilin Liu

  2. Department of Computer Science, CINVESTAV-IPN, Mexico City, 07360, Mexico

    Xiaoou Li

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  1. Yao Li
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  2. Huiyuan Cui
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Correspondence to Huilin Liu.

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Li, Y., Cui, H., Liu, H. et al. Triple-layer attention mechanism-based network embedding approach for anchor link identification across social networks. Neural Comput & Applic 34, 2811–2829 (2022). https://doi.org/10.1007/s00521-021-06556-9

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