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A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial Attacks

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Published:07 November 2022Publication History
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Abstract

Anchor link prediction across social networks plays an important role in multiple social network analysis. Traditional methods rely heavily on user privacy information or high-quality network topology information. These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users’ evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability, and robustness for the scenarios with evasion strategies.

REFERENCES

  1. [1] Cao Shaosheng, Lu Wei, and Xu Qiongkai. 2015. GraRep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 891900.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Cao Shaosheng, Lu Wei, and Xu Qiongkai. 2016. Deep neural networks for learning graph representations. Proceedings of the AAAI Conference on Artificial Intelligence 30, 1 (Feb. 2016).Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Cao Xuezhi and Yu Yong. 2016. BASS: A bootstrapping approach for aligning heterogenous social networks. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 459475.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. [4] Chen Hongxu, Yin Hongzhi, Sun Xiangguo, Chen Tong, Gabrys Bogdan, and Musial Katarzyna. 2020. Multi-level graph convolutional networks for cross-platform anchor link prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 15031511.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Chen Jinyin, Wu Yangyang, Lin Xiang, and Xuan Qi. 2019. Can adversarial network attack be defended?CoRR abs/1903.05994 (2019). arXiv:1903.05994 http://arxiv.org/abs/1903.05994.Google ScholarGoogle Scholar
  6. [6] Chen Jinyin, Wu Yangyang, Xu Xuanheng, Chen Yixian, Zheng Haibin, and Xuan Qi. 2018. Fast gradient attack on network embedding. ArXiv abs/1809.02797 (2018).Google ScholarGoogle Scholar
  7. [7] Cheng Anfeng, Zhou Chuan, Yang Hong, Wu Jia, Li Lei, Tan Jianlong, and Guo Li. 2019. Deep active learning for anchor user prediction. arXiv preprint arXiv:1906.07318 (2019).Google ScholarGoogle Scholar
  8. [8] Chu Xiaokai, Fan Xinxin, Yao Di, Zhu Zhihua, Huang Jianhui, and Bi Jingping. 2019. Cross-network embedding for multi-network alignment. In The World Wide Web Conference. 273284.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. [9] Fortunato Santo. 2010. Community detection in graphs. Physics Reports 486, 3–5 (2010), 75174.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Ghasemian Amir, Hosseinmardi Homa, Galstyan Aram, Airoldi Edoardo M., and Clauset Aaron. 2020. Stacking models for nearly optimal link prediction in complex networks. Proceedings of the National Academy of Sciences 117, 38 (2020), 2339323400.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Goyal Palash and Ferrara Emilio. 2018. Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems 151 (2018), 7894.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Grover Aditya and Leskovec Jure. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 855864.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Guimerà Roger. 2020. One model to rule them all in network science?Proceedings of the National Academy of Sciences 117, 41 (2020), 2519525197.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Guimerà Roger and Sales-Pardo Marta. 2009. Missing and spurious interactions and the reconstruction of complex networks. Proceedings of the National Academy of Sciences 106, 52 (2009), 2207322078.Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Heimann Mark, Shen Haoming, Safavi Tara, and Koutra Danai. 2018. Regal: Representation learning-based graph alignment. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 117126.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Henderson Keith, Gallagher Brian, Eliassi-Rad Tina, Tong Hanghang, Basu Sugato, Akoglu Leman, Koutra Danai, Faloutsos Christos, and Li Lei. 2012. Rolx: Structural role extraction & mining in large graphs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 12311239.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Hoff Peter D., Raftery Adrian E., and Handcock Mark S.. 2002. Latent space approaches to social network analysis. Journal of the American Statistical Association 97, 460 (2002), 10901098.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Iofciu Tereza, Fankhauser Peter, Abel Fabian, and Bischoff Kerstin. 2011. Identifying users across social tagging systems. Proceedings of the International AAAI Conference on Web and Social Media 5, 1 (Jul. 2011), 522525.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Kipf Thomas N. and Welling Max. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).Google ScholarGoogle Scholar
  20. [20] Kitchin Rob. 2016. The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, 2083 (2016), 20160115.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Kollias Giorgos, Mohammadi Shahin, and Grama Ananth. 2011. Network similarity decomposition (NSD): A fast and scalable approach to network alignment. IEEE Transactions on Knowledge and Data Engineering 24, 12 (2011), 22322243.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Kong Xiangnan, Zhang Jiawei, and Yu Philip S.. 2013. Inferring anchor links across multiple heterogeneous social networks. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. 179188.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. [23] Levy Omer and Goldberg Yoav. 2014. Neural word embedding as implicit matrix factorization. Advances in Neural Information Processing Systems 27 (2014), 21772185.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Li Chaozhuo, Wang Senzhang, Wang Yukun, Yu Philip, Liang Yanbo, Liu Yun, and Li Zhoujun. 2019. Adversarial learning for weakly-supervised social network alignment. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 9961003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] Li Xiaoxue, Cao Yanan, Li Qian, Shang Yanmin, Li Yangxi, Liu Yanbing, and Xu Guandong. 2021. RLINK: Deep reinforcement learning for user identity linkage. World Wide Web 24, 1 (2021), 85103.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Liu Jing, Zhang Fan, Song Xinying, Song Young-In, Lin Chin-Yew, and Hon Hsiao-Wuen. 2013. 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. 495504.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Liu Li, Cheung William K., Li Xin, and Liao Lejian. 2016. Aligning users across social networks using network embedding. In IJCAI. 17741780.Google ScholarGoogle Scholar
  28. [28] Liu Li, Li Xin, Cheung William K., and Liao Lejian. 2019. Structural representation learning for user alignment across social networks. IEEE Transactions on Knowledge and Data Engineering 32, 9 (2019), 18241837.Google ScholarGoogle Scholar
  29. [29] Malhotra Anshu, Totti Luam, Jr. Wagner Meira, Kumaraguru Ponnurangam, and Almeida Virgilio. 2012. Studying user footprints in different online social networks. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 10651070.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. [30] Man Tong, Shen Huawei, Huang Junming, and Cheng Xueqi. 2015. Context-adaptive matrix factorization for multi-context recommendation. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 901910.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. [31] Man Tong, Shen Huawei, Liu Shenghua, Jin Xiaolong, and Cheng Xueqi. 2016. Predict anchor links across social networks via an embedding approach. In IJCAI, Vol. 16. 18231829.Google ScholarGoogle Scholar
  32. [32] Michalak Tomasz P., Rahwan Talal, and Wooldridge Michael. 2017. Strategic social network analysis. In Thirty-First AAAI Conference on Artificial Intelligence.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Ou Mingdong, Cui Peng, Pei Jian, Zhang Ziwei, and Zhu Wenwu. 2016. Asymmetric transitivity preserving graph embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 11051114.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Perozzi Bryan, Al-Rfou Rami, and Skiena Steven. 2014. DeepWalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 701710.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Ran Yijun, Liu Si-Yuan, Yu Xiaoyao, Shang Ke-ke, and Jia Tao. 2022. Predicting future links with new nodes in temporal academic networks. Journal of Physics: Complexity (2022).Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Shang Ke-ke, Li Tong-chen, Small Michael, Burton David, and Wang Yan. 2019. Link prediction for tree-like networks. Chaos: An Interdisciplinary Journal of Nonlinear Science 29, 6 (2019), 061103.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Shang Ke-ke and Small Michael. 2022. Link prediction for long-circle-like networks. Physical Review E 105, 2 (2022), 024311.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Shang Yanmin, Kang Zhezhou, Cao Yanan, Zhang Dongjie, Li Yangxi, Li Yang, and Liu Yanbing. 2019. PAAE: A unified framework for predicting anchor links with adversarial embedding. In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 682687.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Shi Chuan, Li Yitong, Zhang Jiawei, Sun Yizhou, and Yu Philip S.. 2016. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering 29, 1 (2016), 1737.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Sun Li, Zhang Zhongbao, Ji Pengxin, Wen Jian, Su Sen, and Philip S. Yu. 2019. DNA: Dynamic social network alignment. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 12241231.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Tan Shulong, Guan Ziyu, Cai Deng, Qin Xuzhen, Bu Jiajun, and Chen Chun. 2014. Mapping users across networks by manifold alignment on hypergraph. Proceedings of the AAAI Conference on Artificial Intelligence 28, 1 (Jun. 2014).Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Tang Jian, Qu Meng, Wang Mingzhe, Zhang Ming, Yan Jun, and Mei Qiaozhu. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. 10671077.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. [43] Maaten Laurens van der and Hinton Geoffrey. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 86 (2008), 25792605.Google ScholarGoogle Scholar
  44. [44] Wang Daixin, Cui Peng, and Zhu Wenwu. 2016. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 12251234.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Wang Shaokai, Li Xutao, Ye Yunming, Feng Shanshan, Lau Raymond Y. K., Huang Xiaohui, and Du Xiaolin. 2019. Anchor link prediction across attributed networks via network embedding. Entropy 21, 3 (2019), 254.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Wang Yaqing, Feng Chunyan, Chen Ling, Yin Hongzhi, Guo Caili, and Chu Yunfei. 2019. User identity linkage across social networks via linked heterogeneous network embedding. World Wide Web 22, 6 (2019), 26112632.Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Waniek Marcin, Michalak Tomasz, and Rahwan Talal. 2020. Hiding in multilayer networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 10211028.Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Waniek Marcin, Michalak Tomasz P., Wooldridge Michael J., and Rahwan Talal. 2018. Hiding individuals and communities in a social network. Nature Human Behaviour 2, 2 (2018), 139147.Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Waniek Marcin, Zhou Kai, Vorobeychik Yevgeniy, Moro Esteban, Michalak Tomasz P., and Rahwan Talal. 2018. Attack tolerance of link prediction algorithms: How to hide your relations in a social network. arXiv preprint arXiv:1809.00152 (2018).Google ScholarGoogle Scholar
  50. [50] Yang Jaewon and Leskovec Jure. 2014. Overlapping communities explain core–periphery organization of networks. Proc. IEEE 102, 12 (2014), 18921902.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Yu Shanqing, Zhao Minghao, Fu Chenbo, Zheng Jun, Huang Huimin, Shu Xincheng, Xuan Qi, and Chen Guanrong. 2019. Target defense against link-prediction-based attacks via evolutionary perturbations. IEEE Transactions on Knowledge and Data Engineering (2019).Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. [52] Zafarani Reza and Liu Huan. 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. 4149.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. [53] Zhan Qianyi, Zhang Jiawei, and Philip Yu S.. 2019. Integrated anchor and social link predictions across multiple social networks. Knowledge and Information Systems 60, 1 (2019), 303326.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. [54] Zhang Jiawei, Yu Philip S., and Zhou Zhi-Hua. 2014. Meta-path based multi-network collective link prediction. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 12861295.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. [55] Zhou Fan, Liu Lei, Zhang Kunpeng, Trajcevski Goce, Wu Jin, and Zhong Ting. 2018. DeepLink: A deep learning approach for user identity linkage. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 13131321.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. [56] Zhou Tao, Ren Jie, Medo Matúš, and Zhang Yi-Cheng. 2007. Bipartite network projection and personal recommendation. Physical Review E 76, 4 (2007), 046115.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Zhou Xiaoping, Liang Xun, Du Xiaoyong, and Zhao Jichao. 2017. Structure based user identification across social networks. IEEE Transactions on Knowledge and Data Engineering 30, 6 (2017), 11781191.Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Zhu Dingyuan, Zhang Ziwei, Cui Peng, and Zhu Wenwu. 2019. Robust graph convolutional networks against adversarial attacks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 13991407.Google ScholarGoogle ScholarDigital LibraryDigital Library

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        • Published in

          cover image ACM Transactions on Privacy and Security
          ACM Transactions on Privacy and Security  Volume 26, Issue 1
          February 2023
          342 pages
          ISSN:2471-2566
          EISSN:2471-2574
          DOI:10.1145/3561959
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Publication History

          • Published: 7 November 2022
          • Online AM: 18 July 2022
          • Accepted: 8 July 2022
          • Revised: 1 June 2022
          • Received: 27 January 2022
          Published in tops Volume 26, Issue 1

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