Abstract
User identity linkage is to identify all the users belonging to the same individual in different networks and has been widely studied along with the increasing popularity of diverse social media sites. Generally, a pair of probable corresponding users on different networks may form a true “Anchor Link”. Most existing methods identify a user based on unique features (username, interests, friends, etc.) and neglect the importance of users local network structure. Therefore, one challenging problem is how to address the user identity linkage problem if only structural information is available. In this paper, we explore techniques for dealing with the fundamental and accumulated information from neighbouring anchor links. Furthermore, we design a Trustworthy Predicting Approach (TPA) for computing the authority of an anchor link, inferring the trustworthiness of a candidate anchor link being true and predicting whether an anchor link is able to be veritably formed. Experiments illustrate the effectiveness of our proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
The conferences selected from the DM field are KDD, SIGMOD, SIGIR, ICDM, ICDE, VLDB, WWW, SDM, CIKM, and WSDM.
- 3.
The conferences selected from the AI field are AAAI, IJCAI, CVPR, ICML, NIPS, UAI, ACL, EMNLP and ECAI.
References
Carmagnola, F., Cena, F.: User identification for cross-system personalisation. Inf. Sci. 179(12), 16–32 (2009)
Ji, S., Li, W., Srivatsa, M., He, J.S., Beyah, R.: Structure based data de-anonymization of social networks and mobility traces. In: Chow, S.S.M., Camenisch, J., Hui, L.C.K., Yiu, S.M. (eds.) ISC 2014. LNCS, vol. 8783, pp. 237–254. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13257-0_14
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 668–677 (1998)
Korula, N., Lattanzi, S.: An efficient reconciliation algorithm for social networks. Proc. VLDB Endow. 7(5), 377–388 (2014)
Kumar, S., Zafarani, R., Liu, H.: Understanding user migration patterns in social media. In: AAAI Conference on Artificial Intelligence, pp. 1204–1209 (2011)
Leskovec, J., Krevl, A.: SNAP datasets: stanford large network dataset collection, June 2014. http://snap.stanford.edu/data
Liu, L., Cheung, W.K., Li, X., Liao, L.: Aligning users across social networks using network embedding. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 1774–1780 (2016)
Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: Hydra: large-scale social identity linkage via heterogeneous behavior modeling. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, pp. 51–62. ACM, New York (2014)
Mu, X., Zhu, F., Wang, J., Wang, J., Wang, J., Zhou, Z.H.: User identity linkage by latent user space modelling. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1775–1784 (2016)
Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 173–187, May 2009
Shen, Y., Jin, H.: Controllable information sharing for user accounts linkage across multiple online social networks. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, pp. 381–390. ACM, New York (2014)
Srivatsa, M., Hicks, M.: Deanonymizing mobility traces: using social network as a side-channel. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, CCS 2012, pp. 628–637. ACM, New York (2012)
Tan, S., Guan, Z., Cai, D., Qin, X., Bu, J., Chen, C.: Mapping users across networks by manifold alignment on hypergraph. In: AAAI Conference on Artificial Intelligence (2014)
Zafarani, R., Liu, H.: Connecting users across social media sites: a behavioral-modeling approach. In: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, vol. Part F128815, pp. 41–49. Association for Computing Machinery, August 2013
Zafarani, R., Tang, L., Liu, H.: User identification across social media. ACM Trans. Knowl. Discov. Data 10(2), 16:1–16:30 (2015)
Zhang, Y., Tang, J., Yang, Z., Pei, J., Yu, P.S.: COSNET: connecting heterogeneous social networks with local and global consistency. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1485–1494 (2015)
Acknowledgements
This work was partially supported by National Natural Science Foundation of China No. U163620068 and Strategy Cooperation Project AQ-1703 and AQ-17014.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Su, Y., Tang, W., Gao, N., Xiang, J. (2018). User Identity Linkage with Accumulated Information from Neighbouring Anchor Links. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-02925-8_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02924-1
Online ISBN: 978-3-030-02925-8
eBook Packages: Computer ScienceComputer Science (R0)