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Tri-Level Cross-Domain Sign Prediction for Complex Network

  • Jiali Pang
  • Donghai Guan
  • Weiwei YuanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

Sign prediction is a fundamental research issue in complex network mining, while the high cost of data collection leads to insufficient data for prediction. The transfer learning method can use the transferable knowledge in other networks to complete the learning tasks in the target network. However, when the inter-domain differences are large, it is difficult for existing methods to obtain useful transferable knowledge. We therefor propose a tri-level cross-domain model using inter-domain similarity and relativity to solve the sign prediction problem in complex networks (TCSP). The first level pre-classifies the source domain, the second level selects the key instances of the source domain, and the third level calculates the similarity between the source domain and the target domain to obtain the pseudo-labels of the target domain. These “labeled” instances are used to train the sign classifier and predict the sign in the target network. Experimental results on real complex network datasets verify the effectiveness of the proposed method.

Keywords

Sign prediction Transfer learning Complex networks 

Notes

Acknowledgement

This research was supported by Nature Science Foundation of China (Grant No. 61672284), Natural Science Foundation of Jiangsu Province (Grant No. BK20171418), China Postdoctoral Science Foundation (Grant No. 2016M591841), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1601225C).

References

  1. 1.
    Ye, J., Cheng, H., Zhu, Z., et al.: Predicting positive and negative links in signed social networks by transfer learning. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1477–1488. ACM (2013)Google Scholar
  2. 2.
    Raina, R., Battle, A., Lee, H., et al.: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine learning. pp. 759–766. ACM (2007)Google Scholar
  3. 3.
    Saito, K., Watanabe, K., Ushiku, Y., et al.: Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3723–3732 (2018)Google Scholar
  4. 4.
    Khodadadi, A., Jalili, M.: Sign prediction in social networks based on tendency rate of equivalent micro-structures. Neurocomputing 257, 175–184 (2017)CrossRefGoogle Scholar
  5. 5.
    Rout, J.K., Choo, K.K.R., Dash, A.K., et al.: A model for sentiment and emotion analysis of unstructured social media text. Electron. Commer. Res. 18, 181–199 (2018)CrossRefGoogle Scholar
  6. 6.
    Chen, W., Zhang, Y., Yeo, C.K., et al.: Unsupervised rumor detection based on users’ behaviors using neural networks. Pattern Recogn. Lett. 105, 226–233 (2018)CrossRefGoogle Scholar
  7. 7.
    Kakisim, A.G., Sogukpinar, I.: Unsupervised binary feature construction method for networked data. Expert Syst. Appl. 121, 256–265 (2019)CrossRefGoogle Scholar
  8. 8.
    Dutta, S., Chandra, V., Mehra, K., et al.: Ensemble algorithms for microblog summarization. IEEE Intell. Syst. 33, 4–14 (2018)CrossRefGoogle Scholar
  9. 9.
    Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. 467, 312–322 (2018)CrossRefGoogle Scholar
  10. 10.
    Mohammadrezaei, M., Shiri, M.E., Rahmani, A.M.: Identifying fake accounts on social networks based on graph analysis and classification algorithms. Secur. Commun. Networks 2018, 8 (2018)Google Scholar
  11. 11.
    Yao, Y., Doretto, G.: Boosting for transfer learning with multiple sources. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1855–1862 (2010)Google Scholar
  12. 12.
    Pan, S.J., Tsang, I.W., Kwok, J.T., et al.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Networks 22, 199–210 (2010)CrossRefGoogle Scholar
  13. 13.
    Noroozi, M., Vinjimoor, A., Favaro, P., et al.: Boosting self-supervised learning via knowledge transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9359–9367 (2018)Google Scholar
  14. 14.
    Wang, L., Geng, X., Ma, X., et al.: Crowd flow prediction by deep spatio-temporal transfer learning (2018). arXiv preprint arXiv:1802.00386
  15. 15.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In Proceedings of the 17th International Conference on Pattern Recognition, 2004, ICPR 2004, vol. 3, pp. 32–36. IEEE (2004)Google Scholar
  16. 16.
    Richardson, M., Agrawal, R., Domingos, P.: Trust Management for the Semantic Web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-39718-2_23CrossRefGoogle Scholar
  17. 17.
    Leskovec, J., Lang, K.J., Dasgupta, A., et al.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6, 29–123 (2009)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Kumar, S., Hooi, B., Makhija, D., et al.: Rev2: fraudulent user prediction in rating platforms. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 333–341. ACM (2018)Google Scholar
  19. 19.
    Kumar, S., Spezzano, F., Subrahmanian, V.S., et al.: Edge weight prediction in weighted signed networks. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 221–230 (2016)Google Scholar
  20. 20.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 641–650 (2010)Google Scholar
  21. 21.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Corroborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

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