Skip to main content
SpringerLink
Log in

FEUI: Fusion Embedding for User Identification across social networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Recognizing identical users across different social networks remains challenging in recent years. Clearly, cross-platform user identification can play promising roles for many applications, such as user behavior prediction, public opinion analysis and e-commerce applications. Representation learning (RL) based methods have received more and more attention in recent years. However, most existing RL based methods only focus on the local structures (i.e., neighbors of vertices), and ignore label information and global structure patterns. Also, the current RL based methods tend to design the user identification and the embedding learning into two separate steps, which will neglect the complex correlations of different information sources. In this paper, we propose a novel approach, named as FEUI (Fusion Embedding for User Identification), by embedding the user-pair-oriented graph (UGP) through jointly integrating network structures, node attribute information and node labels to achieve robust embedding features and predict node labels simultaneously. The FEUI framework contains two modules, dual attribute embedding and joint embedding. These two modules leverage the strong representation ability of an extended auto-encoder and an one-input and two-outputs deep neural network to represent the complex correlations of different information sources. We evaluate our model on two social network datasets with collected user pairs. The experimental results show that the FEUI model can achieve better performance compared with the state-of-the-art approaches.

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
Fig. 10

Similar content being viewed by others

Notes

  1. www.zhihu.com

  2. https://github.com/cpselvis/zhihu-crawler

  3. www.csie.ntu.edu.tw/~cjlin/libsvm

  4. The threshold is set to 0.7 in our experiment.

References

  1. Zafarani R, Liu H (2016) Users joining multiple sites: friendship and popularity variations across sites[J]. Information Fusion 28:83–89

    Article  Google Scholar 

  2. Jain P, Kumaraguru P, Joshi A (2013) @ i seek'fb. me' identifying users across multiple online social networks[C]//Proceedings of the 22nd international conference on World Wide Web. 1259–1268

  3. Zhang Y, Tang J, Yang Z, et al. (2015) Cosnet: Connecting heterogeneous social networks with local and global consistency[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1485–1494

  4. Bartunov S, Korshunov A, Park S T, et al. (2012) Joint link-attribute user identity resolution in online social networks[C]//proceedings of the 6th international conference on knowledge discovery and data mining, workshop on social network mining and analysis. ACM

  5. Man T, Shen H, Liu S, et al. (2016) Predict anchor links across social networks via an embedding approach[C]//IJCAI. 16: 1823-1829

  6. Zhang J, Chen B, Wang X, et al. (2019) Mego2vec: Embedding matched ego networks for user alignment across social networks[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 327–336

  7. Liu D, Wu QY, Han WH et al (2015) User identification across multiple websites based on username features[J]. Chinese J Comput 38(10):2028–2040

    Google Scholar 

  8. Liang W, Meng B, He X et al (2015) GCM: a greedy-based cross-matching algorithm for identifying users across multiple online social networks[C]//Pacific-Asia workshop on intelligence and security informatics. Springer, Cham, pp 51–70

    Google Scholar 

  9. Wang M, Tan Q, Wang X, et al. (2018)De-anonymizing social networks user via profile similarity[C]//2018 IEEE third international conference on data science in cyberspace (DSC). IEEE, 889–895

  10. Narayanan A, Shmatikov V (2009)De-anonymizing social networks[C]//2009 30th IEEE symposium on security and privacy. IEEE, 173–187

  11. Zhou X, Liang X, Zhang H et al (2015)Cross-platform identification of anonymous identical users in multiple social media networks[J]. IEEE Trans Knowl Data Eng 28(2):411–424

    Article  Google Scholar 

  12. Zhou X, Liang X, Du X et al (2017) Structure based user identification across social networks[J]. IEEE Trans Knowl Data Eng 30(6):1178–1191

    Article  Google Scholar 

  13. Korula N, Lattanzi S (2014) An efficient reconciliation algorithm for social networks[J]. Proc VLDB Endow 7(5):377–388

    Article  Google Scholar 

  14. Li Y, Su Z, Yang J, Gao C (2020) Exploiting similarities of user friendship networks across social networks for user identification[J]. Inf Sci 506:78–98

    Article  Google Scholar 

  15. Lee R K W, Hee M S, Prasetyo P K, et al. (2018) Linky: visualizing user identity linkage results for multiple online social networks[C]//2018 IEEE international conference on data mining workshops (ICDMW). IEEE, 1453–1458

  16. Liu S, Wang S, Zhu F (2015) Structured learning from heterogeneous behavior for social identity linkage[J]. IEEE Trans Knowl Data Eng 27(7):2005–2019

    Article  Google Scholar 

  17. Liu X, Chen Y, Fu J (2020) MFRep: joint user and employer alignment across heterogeneous social networks[J]. Neurocomputing 414:36–56

    Article  Google Scholar 

  18. Fu H, Zhang A, Xie X (2015) Effective social graph deanonymization based on graph structure and descriptive information[J]. ACM Trans Intell Syst Technol (TIST) 6(4):1–29

    Article  Google Scholar 

  19. Dong Y, Hu Z, Wang K, et al. (2020) Heterogeneous network representation learning[C]. IJCAI

  20. Tu C, Zeng X, Wang H et al (2018) A unified framework for community detection and network representation learning[J]. IEEE Trans Knowl Data Eng 31(6):1051–1065

    Article  Google Scholar 

  21. Yang C, Xiao Y, Zhang Y, et al. (2020) Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond[J]. arXiv preprint arXiv:2004.00216

  22. Wang Y, Feng C, Chen L, Yin H, Guo C, Chu Y (2019) User identity linkage across social networks via linked heterogeneous network embedding[J]. World Wide Web 22(6):2611–2632

    Article  Google Scholar 

  23. Liu L, Cheung W K, Li X, et al. (2016) Aligning Users across Social Networks Using Network Embedding[C]//IJCAI. 1774–1780

  24. Heimann M, Shen H, Safavi T, et al. (2018) Regal: Representation learning-based graph alignment[C]//Proceedings of the 27th ACM international conference on information and knowledge management. 117–126

  25. Li C, Wang S, Wang Y, et al. (2019) Adversarial learning for weakly-supervised social network alignment[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 33(01): 996–1003

  26. Yang C, Liu Z, Zhao D, Sun M and Chang E Y (2015) Network representation learning with rich text information. In IJCAI, 2111–2117

  27. Wang L, Hu K, Zhang Y, Cao S (2019) Factor graph model based user profile matching across social networks[J]. IEEE Access 7:152429–152442

    Article  Google Scholar 

  28. Wang D, Cui P, Zhu W (2016) Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 1225–1234

  29. Zhang J, Xia C, Zhang C, et al. (2017) BL-MNE: emerging heterogeneous social network embedding through broad learning with aligned AutoEncoder[C]//2017 IEEE international conference on data mining (ICDM). IEEE, 605–614

  30. Jin D, Li B, Jiao P, et al. (2019)Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks[C]//IJCAI. 2663–2669

  31. Liao L, He X, Zhang H, Chua TS (2018) Attributed social network embedding[J]. IEEE Trans Knowl Data Eng 30(12):2257–2270

    Article  Google Scholar 

  32. Balikas G, Amini M R (2016) An empirical study on large scale text classification with skip-gram embeddings[J]. arXiv preprint arXiv:1606.06623

  33. Dunbar RIM, Arnaboldi V, Conti M, Passarella A (2015) The structure of online social networks mirrors those in the offline world[J]. Soc Networks 43:39–47

    Article  Google Scholar 

  34. Liang J, Jacobs P, Sun J et al (2019)Semi-supervised embedding in attributed networks with outliers[C]//proceedings of the 2018 SIAM international conference on data mining. Soc Ind Appl Math:153–161

  35. Mikolov T, Sutskever I, Chen K, et al. (2013) Distributed representations of words and phrases and their compositionality[C]//Advances in neural information processing systems. 3111–3119

  36. Zhang Z, Yang H, Bu J, et al. (2019) ANRL: Attributed Network Representation Learning via Deep Neural Networks[C]//IJCAI. 18: 3155–3161

  37. Dai Q, Shen X, Zhang L, et al. (2019) Adversarial training methods for network embedding[C]//The World Wide Web Conference. 329–339

  38. Z. Yang, W. Cohen, and R. Salakhutdinov. Revisiting semi-supervised learning with graph embeddings. ICML’16

  39. He X, Liao L, Zhang H, et al. (2017) Neural collaborative filtering[C]//Proceedings of the 26th international conference on world wide web. 173–182

  40. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the thirteenth international conference on artificial intelligence and statistics. 249–256

  41. Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE[J]. J Mach Learn Res, 9(11)

Download references

Acknowledgements

This study is supported by Zhejiang Provincial Natural Science Foundation of China under Grant No.LY19F020022, China Knowledge Centre for Engineering Sciences and Technology(CKCEST), Zhejiang Provincial Natural Science Foundation of China under Grant No.LHY21E090004.

Author information

Authors and Affiliations

  1. Qianjiang College, Hangzhou Normal University, Zhejiang, 310018, Hangzhou, China

    Lidong Wang & Keyong Hu

  2. College of Science and Technology, Zhejiang University, Zhejiang, 310012, Hangzhou, China

    Yin Zhang

Authors
  1. Lidong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keyong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yin Zhang.

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

Wang, L., Zhang, Y. & Hu, K. FEUI: Fusion Embedding for User Identification across social networks. Appl Intell 52, 8209–8225 (2022). https://doi.org/10.1007/s10489-021-02716-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02716-5

Keywords

Search

Navigation