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DENA: display name embedding method for Chinese social network alignment

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Abstract

Social network alignment, which aims at finding node correspondences between social networks, is the cornerstone of fusing big data from different social networks. Most of social network alignment solutions are based on English environment. Hence, the existing attribute-based solutions, which contain the unique features in English, are not suitable for Chinese social networks. Although structure-based methods are general, they suffer from the sparsity problem. To solve the Chinese social network alignment problem, in this paper, a novel display name embedding method is proposed, called DENA. It utilizes the morphological and phonetic information of Chinese characters to enhance the alignment accuracy. Specifically, in DENA, a hierarchical n-gram process framework is introduced to generate features from display names and their related morphological information (i.e., strokes) and phonetic information (i.e., pinyin). Then, an innovative graph called display name graph is proposed to transform them into an undirected and unweighted graph. By learning this graph, all features are embedded in to low-dimensional vectors. Therefore, the closeness between embedding vectors of display names represents the probability of the alignment between them. Experiments based on real-world datasets show that DENA outperforms traditional classification-based methods and the state-of-the-art word embedding methods in social network alignment.

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Data availability

The social network datasets used in experiments and source code of DENA are available in https://github.com/Yaoneu/DENA.

Notes

  1. https://www.weibo.com/.

  2. https://www.douban.com/.

  3. https://en.wikipedia.org/wiki/Stroke_order.

  4. https://en.wikipedia.org/wiki/Pinyin.

  5. https://www.zhihu.com/.

References

  1. Ahmed MHM, Chen Q, Li Z (2020) Constructing domain-dependent sentiment dictionary for sentiment analysis. Neural Comput Appl 32(18):14719–14732. https://doi.org/10.1007/s00521-020-04824-8

    Article  Google Scholar 

  2. Bhatia N (2010) Vandana: survey of nearest neighbor techniques. CoRR abs/1007.0085. http://arxiv.org/abs/1007.0085

  3. Cao S, Lu W, Zhou J, Li X (2018) cw2vec: learning Chinese word embeddings with stroke n-gram information. In: Proceedings of the AAAI conference on artificial intelligence, vol 32. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17444

  4. Cao X, Yu Y (2016) Bass: a bootstrapping approach for aligning heterogenous social networks. In: Frasconi P, Landwehr N, Manco G, Vreeken J (eds) Machine learning and knowledge discovery in databases. Springer, Cham, pp 459–475. https://doi.org/10.1007/978-3-319-46128-1_29

    Chapter  Google Scholar 

  5. Chen X, Xu L, Liu Z, Sun M, Luan H (2015) Joint learning of character and word embeddings. In: Twenty-fourth international joint conference on artificial intelligence, pp 1236–1242. http://ijcai.org/Abstract/15/178

  6. Chu X, Fan X, Zhu Z, Bi J (2021) Variational cross-network embedding for anonymized user identity linkage. In: Proceedings of the 30th ACM international conference on information & knowledge management, CIKM ’21, pp 2955–2959. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3459637.3482214

  7. Diao M, Zhang Z, Su S, Gao S, Cao H (2020) Upon: user profile transferring across networks. In: Proceedings of the 29th ACM international conference on information & knowledge management, CIKM ’20, pp 265–274. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3340531.3411964

  8. Du X, Yan J, Zhang R, Zha H (2022) Cross-network skip-gram embedding for joint network alignment and link prediction. IEEE Trans Knowl Data Eng 34(3):1080–1095. https://doi.org/10.1109/TKDE.2020.2997861

    Article  Google Scholar 

  9. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds.) Advances in neural information processing systems, vol. 30. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf

  10. Heimann M, Shen H, Safavi T, Koutra D (2018) Regal: representation learning-based graph alignment. In: Proceedings of the 27th ACM international conference on information and knowledge management, CIKM ’18, pp 117–126. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3269206.3271788

  11. Keikha MM, Rahgozar M, Asadpour M, Abdollahi MF (2020) Influence maximization across heterogeneous interconnected networks based on deep learning. Expert Syst Appl 140:112905. https://doi.org/10.1016/j.eswa.2019.112905

    Article  Google Scholar 

  12. Kong X, Zhang J, Yu PS (2013) Inferring anchor links across multiple heterogeneous social networks. In: Proceedings of the 22nd ACM international conference on information & knowledge management, CIKM ’13, pp 179–188. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2505515.2505531

  13. Li B, Pi D (2020) Network representation learning: a systematic literature review. Neural Comput Appl 32(21):16647–16679. https://doi.org/10.1007/s00521-020-04908-5

    Article  Google Scholar 

  14. Li Q, Han Z, Wu X (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: McIlraith SA, Weinberger KQ (eds.) Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), the 30th innovative applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2–7, 2018, pp 3538–3545. AAAI Press. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16098

  15. Li Y, Cui H, Liu H, Li X (2022) Triple-layer attention mechanism-based network embedding approach for anchor link identification across social networks. Neural Comput Appl 34(4):2811–2829. https://doi.org/10.1007/s00521-021-06556-9

    Article  Google Scholar 

  16. Li Y, Peng Y, Zhang Z, Wu M, Xu Q, Yin H (2018) A deep dive into user display names across social networks. Inf Sci 447:186–204. https://doi.org/10.1016/j.ins.2018.02.072

    Article  Google Scholar 

  17. Li Y, Peng Y, Zhang Z, Xu Q, Yin H (2017) Understanding the user display names across social networks. In: Proceedings of the 26th international conference on world wide web companion, WWW ’17 Companion, pp 1319–1326. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. https://doi.org/10.1145/3041021.3051146

  18. 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. https://doi.org/10.1016/j.ins.2019.08.022

    Article  Google Scholar 

  19. Liu L, Cheung WK, Li X, Liao L (2016) Aligning users across social networks using network embedding. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, pp 1774–1780. http://www.ijcai.org/Abstract/16/254

  20. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: 1st international conference on learning representations, ICLR 2013, Scottsdale, Arizona, USA, May 2–4, 2013, Workshop Track Proceedings. http://arxiv.org/abs/1301.3781

  21. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26:3111–3119

    Google Scholar 

  22. Peled O, Fire M, Rokach L, Elovici Y (2016) Matching entities across online social networks. Neurocomputing 210:91–106. https://doi.org/10.1016/j.neucom.2016.03.089

    Article  Google Scholar 

  23. Shang Y, Kang Z, Cao Y, Zhang D, Li Y, Li Y, Liu Y (2019) Paae: a unified framework for predicting anchor links with adversarial embedding. In: 2019 IEEE international conference on multimedia and Expo (ICME), pp 682–687. https://doi.org/10.1109/ICME.2019.00123

  24. Sun L, Zhang Z, Zhang J, Wang F, Du Y, Su S, Yu PS (2020) Perfect: a hyperbolic embedding for joint user and community alignment. In: 2020 IEEE international conference on data mining (ICDM), pp 501–510. https://doi.org/10.1109/ICDM50108.2020.00059

  25. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, WWW ’15, pp 1067–1077. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. https://doi.org/10.1145/2736277.2741093

  26. Xiong Z, Qin K, Yang H, Luo G (2021) Learning Chinese word representation better by cascade morphological n-gram. Neural Comput Appl 33(8):3757–3768. https://doi.org/10.1007/s00521-020-05198-7

    Article  Google Scholar 

  27. Zafarani R, Liu H (2013) Connecting users across social media sites: a behavioral-modeling approach. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’13, pp 41–49. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2487575.2487648

  28. Zhang S, Tong H (2019) Attributed network alignment: problem definitions and fast solutions. IEEE Trans Knowl Data Eng 31(9):1680–1692. https://doi.org/10.1109/TKDE.2018.2866440

    Article  Google Scholar 

  29. Zhang S, Tong H, Jin L, Xia Y, Guo Y (2021) Balancing consistency and disparity in network alignment. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, KDD ’21, pp 2212–2222. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3447548.3467331

  30. Zhang S, Tong H, Xia Y, Xiong L, Xu J (2020) NetTrans: neural cross-network transformation. Association for Computing Machinery, New York, NY, USA, pp 986–996. https://doi.org/10.1145/3394486.3403141

  31. Zhang S, Tong H, Xu J, Hu Y, Maciejewski R (2019) Origin: non-rigid network alignment. In: 2019 IEEE international conference on big data (big data), pp 998–1007. https://doi.org/10.1109/BigData47090.2019.9005663

  32. Zhang Y, Liu Y, Zhu J, Wu X (2021) Fsprm: a feature subsequence based probability representation model for Chinese word embedding. IEEE/ACM Trans Audio Speech Lang Process 29:1702–1716. https://doi.org/10.1109/TASLP.2021.3073868

    Article  Google Scholar 

  33. Zhang Y, Liu Y, Zhu J, Zheng Z, Liu X, Wang W, Chen Z, Zhai S (2019) Learning Chinese word embeddings from stroke, structure and pinyin of characters. In: Proceedings of the 28th ACM international conference on information and knowledge management, CIKM ’19. Association for Computing Machinery, New York, NY, USA, pp 1011–1020. https://doi.org/10.1145/3357384.3358005

  34. Zhao W, Tan S, Guan Z, Zhang B, Gong M, Cao Z, Wang Q (2018) Learning to map social network users by unified manifold alignment on hypergraph. IEEE Trans Neural Netw Learn Syst 29(12):5834–5846. https://doi.org/10.1109/TNNLS.2018.2812888

    Article  MathSciNet  Google Scholar 

  35. Zhou Y, Ren J, Jin R, Zhang Z, Zheng J, Jiang Z, Yan D, Dou D (2021) Unsupervised adversarial network alignment with reinforcement learning. ACM Trans Knowl Discov Data. https://doi.org/10.1145/3477050

    Article  Google Scholar 

  36. Zhu J, Wang X, Liu Q, Li X, Shao C, Zhou B (2020) A multiview approach based on naming behavioral modeling for aligning Chinese user accounts across multiple networks. Concurr Comput Pract Exp 32(22):e5819. https://doi.org/10.1002/cpe.5819

    Article  Google Scholar 

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Acknowledgments

The work has been supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61472069, 61402089, 61332006 and U1401256; the Fundamental Research Funds for the Central Universities under Grant No. N161602003.

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  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Yao Li & Huilin Liu

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  1. Yao Li
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Correspondence to Huilin Liu.

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Li, Y., Liu, H. DENA: display name embedding method for Chinese social network alignment. Neural Comput & Applic 35, 7443–7461 (2023). https://doi.org/10.1007/s00521-022-08014-6

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