Advertisement

Network Embedding Attack: An Euclidean Distance Based Method

Chapter
  • 80 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12647)

Abstract

Network embedding methods are widely used in graph data mining. This chapter proposes a Genetic Algorithm (GA) based Euclidean Distance Attack strategy (EDA) to attack the DeepWalk-based network embedding to prevent certain structural information from being discovered. EDA disrupts the Euclidean distance between pairs of nodes in the embedding space by making a minimal modification of the network structure, thereby rendering downstream network algorithms ineffective, because a large number of network embedding based downstream algorithms, such as community detection and node classification, evaluate the similarity based on the Euclidean distance between nodes. Different from traditional attack strategies, EDA is an unsupervised network embedding attack method, which does not need labeling information.

Experiments with a set of real networks demonstrate that the proposed EDA method can significantly reduce the performance of DeepWalk-based networking algorithms, outperforming other attack strategies in most cases. The results also indicate the transferability of the EDA method since it works well on attacking the network algorithms based on other network embedding methods such as High-Order Proximity preserved Embedding (HOPE) and non-embedding-based network algorithms such as Label Propagation Algorithm (LPA) and Eigenvectors of Matrices (EM).

Keywords

MDATA Network embedding Euclidean distance attack 

Notes

This work was partially supported by the National Natural Science Foundation of China under Grant No. 61973273 and the Special Scientific Research Fund of Basic Public Welfare Profession of Zhejiang Province under Grant LGF20F020016

References

  1. 1.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  2. 2.
    Hong, R. He, Y., Wu, L., Ge, Y., Wu, X.: Deep attributed network embedding by preserving structure and attribute information. IEEE Trans. Syst. Man Cybern.: Syst. (2019)Google Scholar
  3. 3.
    Barabási, A.-L., et al.: Network Science. Cambridge University Press, Cambridge (2016)zbMATHGoogle Scholar
  4. 4.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  5. 5.
    Andersen, R., Chung, F., Lang, K.: Local graph partitioning using pagerank vectors. In: Null, pp. 475–486. IEEE (2006)Google Scholar
  6. 6.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
  8. 8.
    Athalye, A., Sutskever, I.: Synthesizing robust adversarial examples. arXiv preprint arXiv:1707.07397 (2017)
  9. 9.
    Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372–387. IEEE (2016)Google Scholar
  10. 10.
    Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 (2016)
  11. 11.
    Chen, J., et al.: GA based Q-attack on community detection. arXiv preprint arXiv:1811.00430 (2018)
  12. 12.
    Yu, S., et al.: Target defense against link-prediction-based attacks via evolutionary perturbations. arXiv preprint arXiv:1809.05912 (2018)
  13. 13.
    Zügner, D., Akbarnejad, A., Günnemann, S.: Adversarial attacks on neural networks for graph data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2847–2856. ACM (2018)Google Scholar
  14. 14.
    Chen, J., Wu, Y., Xu, X., Chen, Y., Zheng, H., Xuan, Q.: Fast gradient attack on network embedding. arXiv preprint arXiv:1809.02797 (2018)
  15. 15.
    Wang, X., Eaton, J., Hsieh, C.-J., Wu, F.: Attack graph convolutional networks by adding fake nodes. arXiv preprint arXiv:1810.10751 (2018)
  16. 16.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)Google Scholar
  17. 17.
    Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)Google Scholar
  18. 18.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  19. 19.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  20. 20.
    Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Guerrero, M., Montoya, F.G., Baños, R., Alcayde, A., Gil, C.: Adaptive community detection in complex networks using genetic algorithms. Neurocomputing 266, 101–113 (2017)CrossRefGoogle Scholar
  22. 22.
    Tang, L., Liu, H.: Leveraging social media networks for classification. Data Min. Knowl. Disc. 23(3), 447–478 (2011)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Liu, X., Shen, C., Guan, X., Zhou, Y.: We know who you are: discovering similar groups across multiple social networks. IEEE Trans. Syst. Man Cybern.: Syst. 99, 1–12 (2018)Google Scholar
  24. 24.
    Tang, J., Aggarwal, C., Liu, H.: Node classification in signed social networks. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 54–62. SIAM (2016)Google Scholar
  25. 25.
    Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 115–148. Springer, Boston (2011).  https://doi.org/10.1007/978-1-4419-8462-3_5CrossRefGoogle Scholar
  26. 26.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)Google Scholar
  27. 27.
    Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)Google Scholar
  28. 28.
    Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407 (2018)
  29. 29.
    Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  30. 30.
    Lai, Y.-A., Hsu, C.-C., Chen, W.H., Yeh, M.-Y., Lin, S.-D.: Prune: preserving proximity and global ranking for network embedding. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5257–5266, Curran Associates Inc. (2017)Google Scholar
  31. 31.
    Dai, H., et al.: Adversarial attack on graph structured data. arXiv preprint arXiv:1806.02371 (2018)
  32. 32.
    Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 (2019)
  33. 33.
    Faramondi, L., et al.: Network structural vulnerability: a multiobjective attacker perspective. IEEE Trans. Syst. Man Cybern.: Syst. 99, 1–14 (2018)Google Scholar
  34. 34.
    Waniek, M., Michalak, T.P., Wooldridge, M.J., Rahwan, T.: Hiding individuals and communities in a social network. Nat. Hum. Behav. 2(2), 139 (2018)CrossRefGoogle Scholar
  35. 35.
    Bojcheski, A., Günnemann, S.: Adversarial attacks on node embeddings. arXiv preprint arXiv:1809.01093 (2018)
  36. 36.
    Sun, M., et al.: Data poisoning attack against unsupervised node embedding methods. arXiv preprint arXiv:1810.12881 (2018)
  37. 37.
    Qiu, J., Dong, Y., Ma, H.. Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: unifying DeepWalk, LINE, PTE, and node2vec. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 459–467. ACM (2018)Google Scholar
  38. 38.
    Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009)Google Scholar
  39. 39.
    Morin, F., Bengio, Y.: Hierarchical probabilistic neural network language model. In: Aistats, vol. 5, pp. 246–252. Citeseer (2005)Google Scholar
  40. 40.
    Ghosh, R., Lerman, K.: Community detection using a measure of global influence. In: Giles, L., Smith, M., Yen, J., Zhang, H. (eds.) SNAKDD 2008. LNCS, vol. 5498, pp. 20–35. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14929-0_2CrossRefGoogle Scholar
  41. 41.
    Beveridge, A., Shan, J.: Network of thrones. Math Horizons 23(4), 18–22 (2016)MathSciNetCrossRefGoogle Scholar
  42. 42.
    McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retrieval 3(2), 127–163 (2000)CrossRefGoogle Scholar
  43. 43.
    Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Dreiseitl, S., Ohno-Machado, L.: Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform. 35(5–6), 352–359 (2002)CrossRefGoogle Scholar
  45. 45.
    Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114. ACM (2016)Google Scholar
  46. 46.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)CrossRefGoogle Scholar
  47. 47.
    Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Institue of Cyberspace Security, Zhejiang University of TechnologyHangzhouChina
  2. 2.College of Information EngineeringZhejiang University of TechnologyHangzhouChina
  3. 3.PCL Research Center of Networks and Communications, Peng Cheng LaboratoryShenzhenChina
  4. 4.City University of Hong KongHong KongChina

Personalised recommendations