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Influenced Nodes Discovery in Temporal Contact Network

  • Jinjing Huang
  • Tianqiao Lin
  • An Liu
  • Zhixu Li
  • Hongzhi Yin
  • Lei ZhaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)

Abstract

Information diffusion has been studied for many years to understand how information diffuse in social network or real world. However, which nodes and when they will get influenced are unpredictable because of the uncertainty of information diffusion even we know the initial influenced nodes and diffusion network. Verification is the only way to make sure if a node is influenced or not. The target of discovering influenced nodes is to find more influenced nodes under the limited amount of verifications. In this paper, the temporal contact network is modeled. Then influenced nodes discovery problem in temporal contact network are studied based on the Independent Cascade (IC) model. A path length limited approach is proposed to calculate the infection probability approximately. Experimental results on real and synthetic data sets show our approach has better performance than BFS and Random Walk algorithm.

Keywords

Temporal contact network Information diffusion Influenced nodes discovery 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61572335 and 61572336, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223, the Natural Science Foundation of Jiangsu Provincial Department of Education of China under Grant No. 12KJB520017, and Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.

References

  1. 1.
    Abrahao, B., Chierichetti, F., Kleinberg, R., Panconesi, A.: Trace complexity of network inference. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 491–499. ACM (2013)Google Scholar
  2. 2.
    Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038. ACM (2010)Google Scholar
  3. 3.
    Du, N., Liang, Y., Balcan, M.-F., Song, L.: Influence function learning in information diffusion networks. In: Proceedings of the 31st International Conference on Machine Learning, vol. 2014, p. 2016. NIH Public Access (2014)Google Scholar
  4. 4.
    Gomez Rodriguez, M., Balduzzi, D., Schölkopf, B., Scheffer, G.T. et al.: Uncovering the temporal dynamics of diffusion networks. In: 28th International Conference on Machine Learning (ICML 2011), pp. 561–568. International Machine Learning Society (2011)Google Scholar
  5. 5.
    Gomez Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1019–1028. ACM (2010)Google Scholar
  6. 6.
    Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. ACM SIGMOD Rec. 42(2), 17–28 (2013)CrossRefGoogle Scholar
  7. 7.
    Huang, S., Cheng, J., Wu, H.: Temporal graph traversals: definitions, algorithms, and applications. arXiv preprint arXiv:1401.1919 (2014)
  8. 8.
    Iwata, T., Shah, A., Ghahramani, Z.: Discovering latent influence in online social activities via shared cascade poisson processes. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 266–274 (2013)Google Scholar
  9. 9.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)Google Scholar
  10. 10.
    Kutzkov, K., Bifet, A., Bonchi, F., Gionis, A.: STRIP: stream learning of influence probabilities. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 275–283. ACM (2013)Google Scholar
  11. 11.
    Leskovec, J., Faloutsos, C.: Sampling from large graphs. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 631–636 (2006)Google Scholar
  12. 12.
    Mehdiabadi, M.E., Rabiee, H.R., Salehi, M.: Sampling from diffusion networks. In: Proceedings of the 2012 International Conference on Social Informatics, pp. 106–112. IEEE Computer Society (2012)Google Scholar
  13. 13.
    Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 33–41. ACM (2012)Google Scholar
  14. 14.
    Najar, A., Denoyer, L., Gallinari, P.: Predicting information diffusion on social networks with partial knowledge. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 1197–1204. ACM (2012)Google Scholar
  15. 15.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807–816. ACM (2009)Google Scholar
  16. 16.
    Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1539–1554. ACM (2015)Google Scholar
  17. 17.
    Valiant, L.G.: The complexity of enumeration and reliability problems. SIAM J. Comput. 8, 410–421 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y., Xu, Y.: Path problems in temporal graphs. Proc. VLDB Endow. 7(9), 721–732 (2014)CrossRefGoogle Scholar
  19. 19.
    Zhang, J., Wang, C., Wang, J., Yu, J.X.: Inferring continuous dynamic social influence and personal preference for temporal behavior prediction. Proc. VLDB Endow. 8(3), 269–280 (2014)CrossRefGoogle Scholar
  20. 20.
    Zhang, M., Dai, C., Ding, C., Chen, E.: Probabilistic solutions of influence propagation on social networks. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 429–438. ACM (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jinjing Huang
    • 1
    • 2
  • Tianqiao Lin
    • 1
  • An Liu
    • 1
  • Zhixu Li
    • 1
  • Hongzhi Yin
    • 3
  • Lei Zhao
    • 1
    Email author
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Suzhou Vocational Institute of Industrial TechnologySuzhouChina
  3. 3.School of ITEEUniversity of QueenslandBrisbaneAustralia

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