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Multi Association Semantics-Based User Matching Algorithm Without Prior Knowledge

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The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 813))

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Abstract

Cross-network user matching is one of the basic issues for realizing social network data integration. Existing research based on structure features provides a good matching method for nodes with high-degree, but ignores the matching of nodes with low values. This paper proposes an unsupervised cross-network user matching algorithm based on association semantics to solve the problem of cross-network user matching. It can effectively solve the low-degree user matching problem when the quality of the text type attributes of network users cannot be guaranteed. First, build a social network graph based on multiple types of user behaviors, and define different association semantics according to different behaviors; then, walk based on the association semantics to obtain user node sequences with association semantics, and then combine the network embedding model to learn the user feature vector representation is obtained; finally, the user similarity is measured based on the user vector similarity, and then cross-network user matching is realized. The highlights of this paper are two folds. (1) We treat the association as nodes. (2) Social network user graphs with association semantics can enrich structure feature; walks based on association semantics can obtain walks that are closer to natural language short sentences. The experimental results show that the proposed method can effectively match user with similar behavior characteristics without any prior knowledge, and can effectively achieve the matching of low-degree nodes in heterogeneous networks.

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References

  1. Marco, B., Christian, P., Thorsten, H., Engin, K., Davide, B., Christopher, K.: Abusing social networks for automated user profiling. In: International Workshop on Recent Advances in Intrusion Detection, Berlin, Heidelberg, pp. 422–441 (2010)

    Google Scholar 

  2. Daniele, P., Claude, C., Mohamed, A.K., Pere, M.: How unique and traceable are usernames? In: International Symposium on Privacy Enhancing Technologies Symposium, Berlin, Heidelberg, pp. 1–17 (2011)

    Google Scholar 

  3. Elie, R., Richard, C., Albert, D.: User profile matching in social networks. In: 2010 13th International Conference on Network-Based Information Systems, Japan, pp. 297–304 (2010)

    Google Scholar 

  4. Jing, L., Fan, Z., Xinying, S., Young-In, S., Chin-Yew, L., Hsiao-Wuen, H.: What’s in a name? An unsupervised approach to link users across communities. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, New York, NY, United States, pp. 495–504 (2013)

    Google Scholar 

  5. Kaikai, D., Ling, X., Langshui, Z., Honghai, W., Ping, X., Feifei, G.: A user identification algorithm based on user behavior analysis in social networks. IEEE Access 7, 47114–47123 (2019)

    Article  Google Scholar 

  6. Yongjun, L., Zhen, Z., You, P., Hongzhi, Y., Quanqing, X.: Matching user accounts based on user generated content across social networks. Futur. Gener. Comput. Syst. 83, 104–115 (2018)

    Article  Google Scholar 

  7. Yongjun, L., You, P., Wenli, J., Zhen, Z., Quanqing, X.: User identification based on display names across online social networks. IEEE Access 1319–1326 (2017)

    Google Scholar 

  8. Rong, Z., Jiexun, L., Hsinchun, C., Zan, H.: A framework for authorship identification of online messages: writing-style features and classification techniques. J. Am. Soc. Inform. Sci. Technol. 57, 378–393 (2006)

    Article  Google Scholar 

  9. Mishari, A., Gene, T.: Exploring linkability of user reviews. In: European Symposium on Research in Computer Security, Berlin, Heidelberg, pp. 307–324 (2012)

    Google Scholar 

  10. Chris, R., Yunsung, K., Augustin, C., Nitish, K., Silvio, L.: Linking users across domains with location data: theory and validation. In: Proceedings of the 25th International Conference on World Wide Web, Canada, pp. 707–719 (2016)

    Google Scholar 

  11. Shulong, T., Ziyu, G., Deng, C., Xuzhen, Q., Jiajun, B., Chun, C.: Mapping users across networks by manifold alignment on hypergraph. In: Twenty-Eighth AAAI Conference on Artificial Intelligence, Canada, pp. 159–165 (2014)

    Google Scholar 

  12. Zhongbao, Z., Qihang, G., Tong, Y., Sen, S.: Identifying the same person across two similar social networks in a unified way: globally and locally. Inf. Sci. 394, 53–67 (2017)

    Google Scholar 

  13. Nitish, K., Silvio, L.: An efficient reconciliation algorithm for social networks. In: Proceedings of the VLDB Endowment, vol. 7, pp. 377–388 (2014)

    Google Scholar 

  14. Li, L., William, K.C., Xin, L., Lejian, L.: Aligning users across social networks using network embedding. In: IJCAI2016, New York, USA, pp. 1774–1780 (2016)

    Google Scholar 

  15. Wen, Z., Kai, S., Huang, L., Yalin, W.: Graph neural networks for user identity linkage. arXiv preprint arXiv:1903.02174 (2019)

  16. Xiaoping, Z., Xun, L., Xiaoyong, D., Jichao, Z.: Structure based user identification across social networks. IEEE Trans. Knowl. Data Eng. 30(6), 1178–1191 (2018)

    Article  Google Scholar 

  17. Xiaoping, Z., Xun, L., Haiyan, Z., Yuefeng, M.: Cross-platform identification of anonymous identical users in multiple social media networks. IEEE Trans. Knowl. Data Eng. 28(2), 411–424 (2016)

    Article  Google Scholar 

  18. Wei, Z., Shulong, T., Ziyu, G., et al.: Learning to map social network users by unified manifold alignment on hypergraph. IEEE Trans. Neural Netw. Learn. Syst. 29(12), 5834–5846 (2018)

    Article  MathSciNet  Google Scholar 

  19. Mark, H., Haoming, S., Tara, S., Danai, K.: REGAL: representation learning-based graph alignment. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, New York, NY, USA, pp.117–126 (2018)

    Google Scholar 

  20. Zexuan, Z., Yong, C., Mu, G., Zaiqing, N.: Colink: an unsupervised framework for user identity linkage. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  21. Li, L., Xin, L., William, K.C., Lejian, L.: Structural representation learning for user alignment across social networks. IEEE Trans. Knowl. Data Eng. 32(9), 1824–1837 (2020)

    Google Scholar 

  22. Li, L., Youmin, Z., Shun, F., Fujin, Z., Jun, H., Pu, Z.: ABNE: an attention-based network embedding for user alignment across social networks. IEEE Access 7, 23595–23605 (2019)

    Article  Google Scholar 

  23. Yi-Yu, L., Jennifer, N.: MERL: multi-view edge representation learning in social networks. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 675–684. Association for Computing Machinery, New York, NY, USA (2020)

    Google Scholar 

  24. Weiqing, W., Hongzhi, Y., Xingzhong, D., Wen, H., Yongjun, L., Quoc, V.H.N.: Online User representation learning across heterogeneous social networks. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19), pp. 545–554. Association for Computing Machinery, New York, NY, USA (2019)

    Google Scholar 

  25. Tomas, M., Kai, C., Greg, C., Jeffrey, D.: Efficient estimation of word representations in vector space. In: Proceedings of Workshop at ICLR (2013)

    Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China

    Qiuyan Jiang & Daofu Gong

  2. College of Science and Information, Qingdao Agricultural University, Qingdao, 266001, China

    Qiuyan Jiang

Authors
  1. Qiuyan Jiang
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  2. Daofu Gong
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Corresponding author

Correspondence to Daofu Gong .

Editor information

Editors and Affiliations

  1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China

    Jian Yao

  2. Department of Computer Science, University of Alabama, Tuscaloosa, AL, USA

    Yang Xiao

  3. National University of Defense Technology, Changsha, Hunan, China

    Peng You

  4. Big Data Institute, Hunan University of Finance and Economics, Changsha, Hunan, China

    Guang Sun

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Jiang, Q., Gong, D. (2022). Multi Association Semantics-Based User Matching Algorithm Without Prior Knowledge. In: Yao, J., Xiao, Y., You, P., Sun, G. (eds) The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021). Lecture Notes in Electrical Engineering, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-16-6963-7_30

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  • DOI: https://doi.org/10.1007/978-981-16-6963-7_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-6962-0

  • Online ISBN: 978-981-16-6963-7

  • eBook Packages: EngineeringEngineering (R0)

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