Skip to main content
SpringerLink
Log in

Social network alignment: a bi-layer graph attention neural networks based method

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The task of social network alignment is to identify the user nodes which are active in multiple social networks simultaneously, thus the information from multiple social networks can be integrated to conduct some downstream tasks. Existing network alignment methods mostly establish anchor link prediction models using user profile information or the structural similarities in social networks, which may not effectively model user nodes, thus affecting the effectiveness of social network alignment. This paper focuses on social network alignment by modeling user nodes based on the structural information of irregular graphs and multi-dimensional user features. Specially, Graph Neural Networks (GNNs) models and bi-layer graph attention mechanism are designed to learn the embedding vectors of user nodes in social networks. First, multi-dimensional user features are comprehensively modeled. Then, a user-layer attention mechanism and a feature-layer attention mechanism based on GNN are respectively designed to learn the embedding vectors of user nodes, and the embedding vectors of user nodes in social networks are learned by designing a gated neural network to automatically learn the weight parameters of the user-layer embedding vectors and the feature-layer embedding vectors. Finally, based on the embedding vectors, a bi-directional alignment strategy is proposed to predict the anchor links between the source and target social networks, thus to ensure which meet the constraints of one-to-one alignment relationship. Simulation experiments based on multiple real social network datasets prove that our proposed method achieves better results in metrics of Accuracy and F1 than the existing mainstream social network alignment methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Kong X, Zhang J, Yu PS (2013) Inferring anchor links across multiple heterogeneous social networks. In: Proc. of the 22nd ACM CIKM, pp. 179–188

  2. Zhang J, Chen J, Zhi S, Chang Y, Yu PS, Han J (2017) Link prediction across aligned networks with sparse and low rank matrix estimation. In: IEEE 33rd ICDE, pp. 971–982

  3. Lu C-T, Xie S, Shao W, He L, Yu PS (2016) Item recommendation for emerging online businesses. In: Proc. of the 25th IJCAI, pp. 3797–3803

  4. Zhan Q, Zhang J, Wang S, Yu PS, Xie J (2015) Influence maximization across partially aligned heterogeneous social networks. In: Proc. of Advances in Knowledge Discovery and Data Mining. Springer International Publishing, pp 58–69

  5. Wu J, Zhao Z, Sun Q, Hamido F (2021) A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network. Inf Fusion 67:80–93

  6. Zhang J, Yu PS (2015) Integrated anchor and social link predictions across social networks. In: Proc. of the 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, pp. 2125–2131

  7. Zhang J, Yu PS (2015) Multiple anonymized social networks alignment. In: Proc. of the 2015 ICDM, Washington, DC, USA, pp. 599–608

  8. Liu J, Zhang F, Song X, Song Y-I, Lin C-Y, Hon H-W (2013) What’s in a name?: An unsupervised approach to link users across communities. In: Proc. of the Sixth ACM WSDM, New York, NY, USA, pp. 495–504

  9. Liu S, Wang S, Zhu F, Zhang J, Krishnan R (2014) HYDRA: Large-scale social identity linkage via heterogeneous behavior modeling. In: Proc. of the 2014 ACM SIGMOD, New York, NY, USA, pp. 51–62

  10. Tan S, Guan Z, Cai D, Qin X, Bu J, Chen C (2014) Mapping users across networks by manifold alignment on hypergraph. In: Proc. of the 28th AAAI, Québec city, Québec, Canada, pp. 159–165

  11. Zhang H, Kan M-Y, Liu Y, Ma S (2014) Online social network profile linkage. In: Asia Information Retrieval Symposium. Springer International Publishing, pp 197–208

  12. Zhou X, Liang X, Zhang H, Ma Y (Feb. 2016) Cross-platform identification of anonymous identical users in multiple social media networks. IEEE Trans Knowl Data Eng 28(2):411–424

    Article  Google Scholar 

  13. Liu L, Cheung WK, Li X, Liao L (2016) Aligning users across social networks using network embedding. In: Proc. of the 25th IJCAI, New York, USA, pp. 1774–1780

  14. Zhang W, Shu K, Liu H, Wang Y (2019) Graph neural networks for user identity linkage, ArXiv190302174 Cs

  15. Zhao W, Tan S, Guan Z, Zhang B, Gong M, Cao Z, Wang Q (Dec. 2018) Learning to map social network users by unified manifold alignment on hypergraph. IEEE Trans Neural Netw Learn Syst 29(12):5834–5846

    Article  MathSciNet  Google Scholar 

  16. Zhang Y, Tang J, Yang Z, Pei J, Yu PS (2015) COSNET: Connecting heterogeneous social networks with local and global consistency. In: Proc. of the 21th ACM SIGKDD, New York, USA, pp. 1485–1494

  17. Zhou X, Liang X, Du X, Zhao J (Jun. 2018) Structure based user identification across social networks. IEEE Trans Knowl Data Eng 30(6):1178–1191

    Article  Google Scholar 

  18. Xie W, Mu X, Lee RK-W, Zhu F, Lim E-P (2018) Unsupervised user identity linkage via factoid embedding. In: 2018 ICDM, pp. 1338–1343

  19. Zafarani R, Tang L, Liu H (Oct. 2015) User identification across social media. ACM Trans Knowl Discov Data 10(2):16:1–16:30

    Article  Google Scholar 

  20. Feng S, Shen D, Nie T, Kou Y, He J, Yu G (2018) Inferring anchor links based on social network structure. IEEE Access 6:17340–17353

    Article  Google Scholar 

  21. Qu Y, Sun Z, Hu W, Zhang Q (2018) Bootstrapping entity alignment with knowledge graph embedding. In: 2018 IJCAI, pp. 4396–4402

  22. Cheng A, Liu C-Y, Zhou C, Tan J, Guo L (2018) User alignment via structural interaction and propagation. In: 2018 IJCNN, pp. 1–8

  23. Huynh TT, Tong VV, Duong CT, Huynh Quyet T, Nguyen QVH, Sattar A (2019) Network alignment by representation learning on structure and attribute. In: PRICAI 2019: Trends in Artificial Intelligence, Cham, pp. 698–711

  24. Fu S, Wang G, Xia S, Liu L (2020) Deep multi-granularity graph embedding for user identity linkage across social networks. Knowl-Based Syst 193:105301

    Article  Google Scholar 

  25. Li X, Cao Y, Shang Y, Li Y, Liu Y, Tan J (2021) RLINK: deep reinforcement learning for user identity linkage. World Wide Web 24:85–103

    Article  Google Scholar 

  26. Zhang J et al (2018) MEgo2Vec: Embedding matched ego networks for user alignment across social networks. In: Proc. of the 27th ACM CIKM, New York, NY, USA, pp. 327–336

  27. Zhou F, Wen Z, Zhong T, Trajcevski G, Liu L (2020) Unsupervised user identity linkage via graph neural networks. In: Proc. 2020 IEEE global communications conference. IEEE

  28. Bahdanau D, Cho K, Bengio Y (May 2016) Neural machine translation by jointly learning to align and translate. ArXiv14090473 Cs stat

  29. Hu H, Zhang Z, Xie Z, Lin S (Apr. 2019) Local relation networks for image recognition, ArXiv190411491 Cs

  30. Yu S, Wang Y, Yang M, Li B, Qu Q, Shen J (2019) NAIRS: A neural attentive interpretable recommendation system. In: Proc. of the 12th ACM WSDM, New York, NY, USA, pp. 790–793

  31. J. Lu, J. Yang, D. Batra, and D. Parikh, (2016) Hierarchical question-image co-attention for visual question answering, in advances in neural information processing systems 29, Eds. Curran Associates, Inc., pp. 289–297

  32. Le QV, Mikolov T (May 2014) Distributed representations of sentences and documents. In: 2014 International Conference on Machine Learning, pp. 1188–1196

  33. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (Feb. 2018) Graph attention networks, ArXiv171010903 Cs stat

  34. Wang D, Cui P, Zhu W (2016) Structural deep network embedding, In: Proc. of the 22nd ACM SIGKDD, New York, NY, USA, pp. 1225–1234

  35. Heimann M, Shen H, Safavi T, Koutra D (2018) REGAL: Representation learning-based graph alignment. In: Proc. 27th ACM –CIKM, pp. 117–126

  36. Man T, Shen H, Liu S, Jin X, Cheng X (2016) Predict anchor links across social networks via an embedding approach. In: Proc. of the 25th IJCAI, New York, USA, pp. 1823–1829

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Meilian Lu, Yinlong Dai & Zhiqiang Zhang

Authors
  1. Meilian Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yinlong Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meilian Lu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, M., Dai, Y. & Zhang, Z. Social network alignment: a bi-layer graph attention neural networks based method. Appl Intell 52, 16310–16333 (2022). https://doi.org/10.1007/s10489-022-03216-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03216-w

Keywords

Search

Navigation