Skip to main content
SpringerLink
Log in

Identifying multiple social network accounts belonging to the same users

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Social networks used for different purposes by users contain different user data. Finding same users' accounts in different social networks and compiling the data found in a single repository will be a very important factor that will both improve the recommended systems and increase the user experience. The aim of this study is to collect the data of thousands of users in nine different social networks and to find the same users in these networks. For this purpose, the novel node alignment and node similarity methods were proposed in the study. While using the anchor method for topological-based node proposition, density relationships between connections are also taken into account. In the node similarity method, the number of successful node matching was increased with attribute selection criteria and initial state selection method we proposed. However, in this study, alignment and similarity were determined both according to users’ profile characteristics and their relationship with other users. Nine different methods have been proposed for finding the same accounts on different social networks. The proposed methods were tested on the data of two to six social networks, and users' match success rates were measured. The results showed success rates of up to 95%. This enabled the creation of a wide user profile covering multiple social networks for users whose different attributes are gathered on the same graph in multiple social networks.

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

Similar content being viewed by others

References

  • Adamic L, Adar E (2005) How to search a social network. Soc Netw 27(3):187–203

    Article  Google Scholar 

  • Aslan S, Kaya M (2018) Topic recommendation for authors as a link prediction problem. Futur Gener Comput Syst 89:249–264

    Article  Google Scholar 

  • Bag S, Kumar SK, Tiwari MK (2019) An efficient recommendation generation using relevant Jaccard similarity. Inf Sci 483:53–64

    Article  Google Scholar 

  • Berlingerio M, Koutra D, Eliassi-Rad T, Faloutsos C (2012) Netsimile: a scalable approach to size-independent network similarity. arXiv:1209.2684

  • Bródka P (2016) A method for group extraction and analysis in multilayer social networks. PhD Thesis, Wroclaw University of Technology

  • Bütün E, Kaya M (2019) A pattern based supervised link prediction in directed complex networks. Physica A 525:1136–1145

    Article  Google Scholar 

  • Bütün E, Kaya M (2020) Predicting citation count of scientists as a link prediction problem. IEEE Transact Cybern 50(10):4518–4529

    Article  Google Scholar 

  • Bütün E, Kaya M, Alhajj R (2018) Extension of neighbor-based link prediction methods for directed, weighted and temporal social networks. Inf Sci 463:152–165

    Article  MathSciNet  Google Scholar 

  • Cai B, Wang H, Zheng H, Wang H (2011) An improved random walk based clustering algorithm for community detection in complex networks. Paper presented at IEEE international conference on systems, man, and cybernetics. pp. 2162–21

  • Deng K, Xing L, Zheng L, Wu H, Xie P, Gao F (2019) A user identification algorithm based on user behavior analysis in social networks. IEEE Access 7:47114–47123

    Article  Google Scholar 

  • Dong Y, et al. (2012) Link prediction and recommendation across heterogeneous social networks. Paper presented at IEEE 12th International conference on data mining. pp. 181–190

  • Donnat C, Zitnik M, Hallac D, Leskovec J (2018) Learning structural node embeddings via diffusion wavelets. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1320–1329

  • Faisal FE, Zhao H, Milenković T (2014) Global network alignment in the context of aging. IEEE/ACM Trans Comput Biol Bioinf 12(1):40–52

    Article  Google Scholar 

  • Fishkind DE, Adali S, Patsolic HG, Meng L, Singh D, Lyzinski V, Priebe CE (2019) Seeded graph matching. Pattern Recogn 87:203–215

    Article  Google Scholar 

  • Fouss F, Pirotte A, Renders JM, Saerens M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng 19(3):355–369

    Article  Google Scholar 

  • Jeh G, Widom J (2002) SimRank: a measure of structural-context similarity. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 538–543

  • Kasbekar P, Potika K, Pollett C (2020) Find me if you can: aligning users in different social networks. Paper presented at IEEE sixth international conference on big data computing service and applications (big data service). pp. 46–53

  • Kleinberg J (2000) The small-world phenomenon: an algorithmic perspective. In Proceedings of the thirty-second annual ACM symposium on Theory of computing. pp. 163–170

  • Koutra D, Tong H, Lubensky D (2013) Big-align: fast bipartite graph alignment. In: 2013 IEEE 13th international conference on data mining, pp 389–398

  • Koutra D, Parikh A, Ramdas A, Xiang J (2016) Algorithms for graph similarity and subgraph matching. Phys Rev Lett

  • Kuchaiev O, Pržulj N (2011) Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 27(10):1390–1396

    Article  Google Scholar 

  • Lacoste-Julien S, Palla K, Davies A, Kasneci G, Graepel T, Ghahramani Z (2013) Sigma: simple greedy matching for aligning large knowledge bases. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 572–580

  • Le DH (2017) Random walk with restart: a powerful network propagation algorithm in bioinformatics field. Paper presented at 4th NAFOSTED conference on information and computer science. pp. 242–247

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

    Article  Google Scholar 

  • Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inform Sci Technol 58(7):1019–1031

    Article  Google Scholar 

  • Liu S, Wang S, Zhu F, Zhang J, Krishnan R (2014) Hydra: large-scale social identity linkage via heterogeneous behavior modeling. In: Proceedings of the 2014 ACM SIGMOD international conference on Management of data. pp. 51–62

  • Liu L, Cheung WK, Li X, Liao L (2016) Aligning users across social networks using network embedding. In Ijcai (pp. 1774–1780).

  • Peyer S, S., Rautenbach, D., & Vygen, J. (2009) A generalization of Dijkstra’s shortest path algorithm with applications to VLSI routing. J Discret Algorithms 7(4):377–390

    Article  MathSciNet  Google Scholar 

  • Rahman MM (2012) Intellectual knowledge extraction from online social data. Paper presented at 2012 international conference on informatics, electronics & vision (ICIEV). pp. 205–210

  • Ribeiro LFR, Saverese PHP, Figueiredo DR. (2017) Struc2vec: learning node representations from structural identity. DOI: https://doi.org/10.1145/3097983.3098061

  • Sun Y, Crawford J, Tang J, Milenković T (2015) Simultaneous optimization of both node and edge conservation in network alignment via WAVE. In: Pop M, Touzet H (eds) International workshop on algorithms in bioinformatics. Springer, Berlin, Heidelberg, pp 16–39

    Chapter  Google Scholar 

  • Symeonidis P, Tiakas E (2014) Transitive node similarity: predicting and recommending links in signed social networks. World Wide Web 17(4):743–776

    Article  Google Scholar 

  • Tan S, Guan Z, Cai D, Qin X, Bu J, Chen C (2014) Mapping users across networks by manifold alignment on hypergraph. Paper presented at twenty-eighth AAAI conference on artificial intelligence

  • Wang M, Tan Q, Wang X, Shi J (2018) De-anonymizing social networks user via profile similarity. In 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) (pp. 889–895). IEEE.

  • Wang, H., Zhou, B., Huang, J., Liu, Y., Zheng, X., & Han, W. (2019). BICON: Connecting the Same Users of Different Social Networks using BiLSTM. In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC) (pp. 432–438). IEEE.

  • Wu, S. H., Chien, H. H., Lin, K. H., & Yu, P. (2014). Learning the consistent behavior of common users for target node prediction across social networks. In International Conference on Machine Learning (pp. 298–306).

  • Yang J, Zhang XD (2016) Predicting missing links in complex networks based on common neighbors and distance. Scientific reports 6:38208

    Article  Google Scholar 

  • Zager LA, Verghese GC (2008) Graph similarity scoring and matching. Appl Math Lett. https://doi.org/10.1016/j.aml.2007.01.006

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang J, Philip SY (2015) June). Integrated anchor and social link predictions across social networks, In Twenty-fourth international joint conference on artificial intelligence

    Google Scholar 

Download references

Acknowledgement

This work was supported by TUBITAK as a research project under Grant No: 119E309.

Author information

Authors and Affiliations

  1. Department of Software Engineering, Ostim Technical University, 06374, Ankara, Turkey

    Ahmet Anıl Müngen

  2. Department of Computer Engineering, Fırat University, 23119, Elazig, Turkey

    Esra Gündoğan & Mehmet Kaya

Authors
  1. Ahmet Anıl Müngen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Esra Gündoğan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehmet Kaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehmet Kaya.

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

Müngen, A.A., Gündoğan, E. & Kaya, M. Identifying multiple social network accounts belonging to the same users. Soc. Netw. Anal. Min. 11, 29 (2021). https://doi.org/10.1007/s13278-021-00736-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-021-00736-0

Keywords

Search

Navigation