Skip to main content

Advertisement

SpringerLink
Log in

HFUL: a hybrid framework for user account linkage across location-aware social networks

  • Regular Paper
  • Published:
The VLDB Journal Aims and scope Submit manuscript

Abstract

Sources of complementary information are connected when we link user accounts belonging to the same user across different platforms or devices. The expanded information promotes the development of a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Due to the significance of user account linkage and the widespread popularization of GPS-enabled mobile devices, there are increasing research studies on linking user account with spatio-temporal data across location-aware social networks. Being different from most existing studies in this domain that only focus on the effectiveness, we propose a novel framework entitled HFUL (A Hybrid Framework for User Account Linkage across Location-Aware Social Networks), where efficiency, effectiveness, scalability, robustness, and application of user account linkage are considered. Specifically, to improve the efficiency, we develop a comprehensive index structure from the spatio-temporal perspective, and design novel pruning strategies to reduce the search space. To improve the effectiveness, a kernel density estimation-based method has been proposed to alleviate the data sparsity problem in measuring users’ similarities. Additionally, we investigate the application of HFUL in terms of user prediction, time prediction, and location prediction. The extensive experiments conducted on three real-world datasets demonstrate the superiority of HFUL in terms of effectiveness, efficiency, scalability, robustness, and application compared with the state-of-the-art 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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Gao, H., Liu, H.: Data analysis on location-based social networks, in Mobile social networking, 2014, pp. 165–194

  2. Pham, H., Shahabi, C., Liu, Y.: Ebm: an entropy-based model to infer social strength from spatiotemporal data, in SIGMOD, 2013, pp. 265–276

  3. Lichman, M., Smyth, P.: Modeling human location data with mixtures of kernel densities, in KDD, 2014, pp. 35–44

  4. Riederer, C., Kim, Y., Chaintreau, A., Korula, N., Lattanzi, S.: Linking users across domains with location data: Theory and validation, in WWW, 2016, pp. 707–719

  5. Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. ICWSM 11, 70–573 (2011)

    Google Scholar 

  6. Wang, W., Yin, H., Sadiq, S., Chen, L., Xie, M., Zhou, X.: Spore: a sequential personalized spatial item recommender system, in ICDE, 2016, pp. 954–965

  7. Zhang, J., Kong, X., Yu, P. S.: Transferring heterogeneous links across location-based social networks, in WSDM, 2014, pp. 303–312

  8. Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects, in KDD, 2010, pp. 1099–1108

  9. Yuan, Q., Cong, G., Ma, Z., Sun, A., Magnenat-Thalmann, N.: Who, where, when and what: discover spatio-temporal topics for twitter users, in KDD, 2013, pp. 605–613

  10. Wand, M.P.: Fast computation of multivariate kernel estimators. J. Comput. Graph. Stat. 3(4), 433–445 (1994)

    Google Scholar 

  11. Lopez-Novoa, U., Sáenz, J., Mendiburu, A., Miguel-Alonso, J.: An efficient implementation of kernel density estimation for multi-core and many-core architectures. J. High Perform. Comput. Appl. 29(3), 331–347 (2015)

    Article  Google Scholar 

  12. Lopez-Novoa, U., Mendiburu, A., Miguel-Alonso, J.: Kernel density estimation in accelerators. J. Supercomput. 72(2), 545–566 (2016)

    Article  Google Scholar 

  13. Chen, W., Yin, H., Wang, W., Zhao, L., Hua, W., Zhou, X.: Exploiting spatio-temporal user behaviors for user linkage, in CIKM, 2017

  14. Chen, W., Yin, H., Wang, W., Zhao, L., Zhou, X.: Effective and efficient user account linkage across location based social networks, in ICDE, 2018, pp. 1085–1096

  15. Shu, K., Wang, S., Tang, J., Zafarani, R., Liu, H.: User identity linkage across online social networks: a review. SIGKDD Explor. 18(2), 5–17 (2017)

    Article  Google Scholar 

  16. Huynh, T. T., Tong, V. V., Nguyen, T. T., Yin, H., Weidlich, M., Hung, N. Q. V.: Adaptive network alignment with unsupervised and multi-order convolutional networks, in ICDE, 2020, pp. 85–96

  17. Zhang, Y., Yin, H., Huang, Z., Du, X., Yang, G., Lian, D.: Discrete deep learning for fast content-aware recommendation, in WSDM, 2018, pp. 717–726

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

    Article  Google Scholar 

  19. Chen, H., Yin, H., Sun, X., Chen, T., Gabrys, B., Musial, K.: Multi-level graph convolutional networks for cross-platform anchor link prediction, in KDD, 2020, pp. 1503–1511

  20. Zafarani, R., Liu, H.: Connecting corresponding identities across communities. ICWSM 9, 354–357 (2009)

    Article  Google Scholar 

  21. Vosecky, J., Hong, D., Shen, V.Y.: User identification across social networks using the web profile and friend network. J. Web Appl. 2(1), 23–34 (2010)

    Google Scholar 

  22. Iofciu, T., Fankhauser, P., Abel, F., Bischoff, K.: Identifying users across social tagging systems. in ICWSM, 2011

  23. Liu, J., Zhang, F., Song, X., Song, Y.-I., Lin, C.-Y., Hon, H.-W.: What’s in a name?: an unsupervised approach to link users across communities, in WSDM, 2013, pp. 495–504

  24. R. Zafarani and H. Liu, Connecting users across social media sites: a behavioral-modeling approach, in KDD, 2013, pp. 41–49

  25. Peled, O., Fire, M., Rokach, L., Elovici, Y.: Entity matching in online social networks, in Social Computing, 2013, pp. 339–344

  26. Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: Hydra: Large-scale social identity linkage via heterogeneous behavior modeling, in KDD, 2014, pp. 51–62

  27. Shen, Y., Jin, H.: Controllable information sharing for user accounts linkage across multiple online social networks, in CIKM, 2014, pp. 381–390

  28. Mu, X., Zhu, F., Lim, E.-P., Xiao, J., Wang, J., Zhou, Z.-H.: User identity linkage by latent user space modelling, in KDD, 2016, pp. 1775–1784

  29. Zhou, F., Liu, L., Zhang, K., Trajcevski, G., Wu, J., Zhong, T.: Deeplink: a deep learning approach for user identity linkage, in INFOCOM, 2018, pp. 1313–1321

  30. Xie, W., Mu, X., Lee, R. K.-W., Zhu, F., Lim, E.-P.: Unsupervised user identity linkage via factoid embedding, in ICDM, 2018, pp. 1338–1343

  31. Liu, L., Zhang, Y., Fu, S., Zhong, F., Hu, J., Zhang, P.: Abne: an attention-based network embedding for user alignment across social networks. IEEE Access 7, 595–605 (2019)

    Google Scholar 

  32. Zhou, J., Fan, J.: Translink: user identity linkage across heterogeneous social networks via translating embeddings, in INFOCOM, 2019, pp. 2116–2124

  33. Fu, S., Wang, G., Xia, S., Liu, L.: Deep multi-granularity graph embedding for user identity linkage across social networks. Knowl. Based Syst. 193, 105301105301 (2020)

    Article  Google Scholar 

  34. Han, X., Wang, L., Xu, L., Zhang, S.: Social media account linkage using user-generated geo-location data, in ISI, 2016, pp. 157–162

  35. Gao, X., Ji, W., Li, Y., Deng, Y., Dong, W.: User identification with spatio-temporal awareness across social networks, in CIKM, 2018, pp. 1831–1834

  36. Jin, F., Hua, W., Xu, J., Zhou, X.: Moving object linking based on historical trace, in ICDE, 2019, pp. 1058–1069

  37. Zhang, W., Lai, X., Wang, J.: Social link inference via multiview matching network from spatiotemporal trajectories, IEEE Transactions on Neural Networks and Learning Systems, pp. 1–12, 2020

  38. Scott, D.W., Sheather, S.J.: Kernel density estimation with binned data. Commun. Stat.-Theory Methods 14(6), 1353–1359 (1985)

    Article  Google Scholar 

  39. Silverman, B. W.: Density estimation for statistics and data analysis, 1986, vol. 26

  40. Zhang, J.-D., Chow, C.-Y.: igslr: personalized geo-social location recommendation: a kernel density estimation approach, in GIS, 2013, pp. 334–343

  41. Hulden, M., Silfverberg, M., Francom, J.: Kernel density estimation for text-based geolocation. in AAAI, 2015, pp. 145–150

  42. Backurs, A., Indyk, P., Wagner, T.: Space and time efficient kernel density estimation in high dimensions, in NeurIPS, 2019, pp. 15 773–15 782

  43. Hohl, A., Chen, P.: Spatiotemporal simulation: local ripley’s K function parameterizes adaptive kernel density estimation, in SIGSPATIAL, 2019, pp. 16–23

  44. Hasan, S., Zhan, X., Ukkusuri, S. V.: Understanding urban human activity and mobility patterns using large-scale location-based data from online social media, In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, 2013

  45. Zhang, P., Deng, M., Van de Weghe, N.: Clustering spatio-temporal trajectories based on kernel density estimation, in ICCSA, 2014, pp. 298–311

  46. Romano, B., Jiang, Z.: Visualizing traffic accident hotspots based on spatial-temporal network kernel density estimation, in SIGSPATIAL, 2017, pp. 1–4

  47. Wang, Z., Liu, L., Zhou, H., Lan, M.: How is the confidentiality of crime locations affected by parameters in kernel density estimation? Int. J. Geo-Inf. 8(12), 544 (2019)

    Article  Google Scholar 

  48. Zhou, Z., Lan, R., Rui, Y., Zhou, J., Dong, L., Cheng, R., Cai, X.: A new acoustic emission source location method using tri-variate kernel density estimator. IEEE Access 7, 379–388 (2019)

    Google Scholar 

  49. Coleman, B., Shrivastava, A.: Sub-linear races sketches for approximate kernel density estimation on streaming data, in WWW, 2020, pp. 1739–1749

  50. Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)

    Article  Google Scholar 

  51. Albanese, A., Pal, S.K., Petrosino, A.: Rough sets, kernel set, and spatiotemporal outlier detection. TKDE 26(1), 194–207 (2014)

    Google Scholar 

  52. Duggimpudi, M.B., Abbady, S., Chen, J., Raghavan, V.V.: Spatio-temporal outlier detection algorithms based on computing behavioral outlierness factor. Data Knowl. Eng. 122, 1–24 (2019)

    Article  Google Scholar 

  53. Begum, N., Ulanova, L., Wang, J., Keogh, E. J.: Accelerating dynamic time warping clustering with a novel admissible pruning strategy, in KDD, 2015, pp. 49–58

  54. Cao, W., Wu, Z., Wang, D., Li, J., Wu, H.: Automatic user identification method across heterogeneous mobility data sources, in ICDE, 2016, pp. 978–989

  55. Y. Theodoridis, J. R. O. Silva, and M. A. Nascimento, On the generation of spatiotemporal datasets, in SSD, vol. 1651, 1999, pp. 147–164

Download references

Acknowledgements

This work is supported by Australian Research Council Future Fellowship (Grant No. FT210100624) and Discovery Project (Grant No. DP190101985). It is partially supported by the National Natural Science Foundation of China under Grant No. 61902270 and No. 62072125, and the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 19KJA610002.

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou, China

    Wei Chen & Lei Zhao

  2. Faculty of Information Technology, Monash University, Melbourne, Australia

    Weiqing Wang

  3. School of ITEE, The University of Queensland, Bribane, Australia

    Hongzhi Yin

  4. The Hong Kong University of Science and Technology, Hong Kong, China

    Xiaofang Zhou

Authors
  1. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiqing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongzhi Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaofang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Zhao.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (rar 2482 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, W., Wang, W., Yin, H. et al. HFUL: a hybrid framework for user account linkage across location-aware social networks. The VLDB Journal 32, 1–22 (2023). https://doi.org/10.1007/s00778-022-00730-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00778-022-00730-8

Keywords

Search

Navigation