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A Fast Climbing Approach for Diffusion Source Inference in Large Social Networks

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Data Science (ICDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9208))

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Abstract

In this era of information explosion, how to discover potential useful information in social networks and further locate the source has become of great importance. However, in front of the large scale social networks, the large calculation cost is the key difficulty in source locating algorithms. Aiming at this problem, we present a fast method based on climbing algorithms to locate the information source with less calculation cost in large scale social networks. Experimental results on both generated and real-world data sets show that our algorithm is more faster than existing algorithms, since it needs fewer iterations.

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References

  1. Comin, C.H., da Fontoura Costa, L.: Identifying the starting point of a spreading process in complex networks. Phys. Rev. E 84(5), 056105 (2011)

    Article  Google Scholar 

  2. Shah, D., Zaman, T.: Rumors in a network: who’s the culprit? IEEE Trans. Inf. Theory 57(8), 5163–5181 (2011)

    Article  MathSciNet  Google Scholar 

  3. Fioriti, V., Chinnici, M.: Predicting the sources of an outbreak with a spectral technique, arXiv preprint arXiv:1211.2333

  4. Agaskar, A., Lu, Y.M.: A fast monte carlo algorithm for source localization on graphs. In: SPIE Optical Engineering+ Applications, International Society for Optics and Photonics, p. 88581N (2013)

    Google Scholar 

  5. Zhang, P., Zhou, C., Wang, P., Gao, B.J., Zhu, X., Guo, L.: E-tree: an efficient indexing structure for ensemble models on data streams. IEEE Trans. Knowl. Data Eng. 27(2), 461–474 (2015)

    Article  Google Scholar 

  6. Zhou, C., Guo, L.: A note on influence maximization in social networks from local to global and beyond. Procedia Comput. Sci. 30, 81–87 (2014)

    Article  Google Scholar 

  7. Volz, E., Meyers, L.A.: Susceptible-infected-recovered epidemics in dynamic contact networks. Proc. Roy. Soc. B: Biol. Sci. 274(1628), 2925–2934 (2007)

    Article  Google Scholar 

  8. Anderson, R.M., May, R.M., Anderson, B.: Infectious Diseases of Humans: Dynamics and Control, vol. 28. Wiley Online Library (1992)

    Google Scholar 

  9. Kimura, M., Saito, K., Nakano, R.: Extracting influential nodes for information diffusion on a social network. In: AAAI, vol. 7, pp. 1371–1376 (2007)

    Google Scholar 

  10. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106. ACM (2001)

    Google Scholar 

  11. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70. ACM (2002)

    Google Scholar 

  12. 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 

  13. Zhou, C., Zhang, P., Guo, J., Zhu, X., Guo, L.: Ublf: an upper bound based approach to discover influential nodes in social networks. In: IEEE 13th International Conference on Data Mining (ICDM), pp. 907–916. IEEE (2013)

    Google Scholar 

  14. Guo, J., Zhang, P., Zhou, C., Cao, Y., Guo, L.: Personalized influence maximization on social networks. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 199–208. ACM (2013)

    Google Scholar 

  15. Zhou, C., Zhang, P., Guo, J., Guo, L.: An upper bound based greedy algorithm for mining top-k influential nodes in social networks. In: Proceedings of the 23rd International Conference on World Wide Web Companion, pp. 421–422. International World Wide Web Conferences Steering Committee (2014)

    Google Scholar 

  16. Zhou, C., Zhang, P., Zang, W., Guo, L.: Maximizing the cumulative influence through a social network when repeat activation exists. Procedia Comput. Sci. 29, 422–431 (2014)

    Article  Google Scholar 

  17. Zhou, C., Zhang, P., Zang, W., Guo, L.: Maximizing the long-term integral influence in social networks under the voter model. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, pp. 423–424. International World Wide Web Conferences Steering Committee (2014)

    Google Scholar 

  18. Zhou, C., Zhang, P., Zang, W., Guo, L.: On the upper bounds of spread for greedy algorithms in social network influence maximization. IEEE Trans. Knowl. Data Eng. 1, p. 1 (PrePrints). doi:10.1109/TKDE.2015.2419659

  19. Yao, Q., Zhou, C., Xiang, L., Cao, Y., Guo, L.: Minimizing the negative influence by blocking links in social networks. In: Lu, Y., Xu, W., Xi, Z. (eds.) ISCTCS 2014. CCIS, vol. 520, pp. 65–73. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  20. Yao, Q., Zhou, C., Shi, R., Wang, P., Guo, L.: Topic-aware social influence minimization. In: 24th International World Wide Web Conference. ACM (2015)

    Google Scholar 

  21. Pinto, P.C., Thiran, P., Vetterli, M.: Locating the source of diffusion in large-scale networks. Phys. Rev. Lett. 109(6), 068702 (2012)

    Article  Google Scholar 

  22. Shah, D., Zaman, T.: Rumor centrality: a universal source detector. ACM SIGMETRICS Perform. Eval. Rev. 40, 199–210 (2012). ACM

    Article  Google Scholar 

  23. Zhu, K., Ying, L.: Information source detection in the sir model: a sample path based approach. In: Information Theory and Applications Workshop (ITA), pp. 1–9. IEEE (2013)

    Google Scholar 

  24. Luo, W., Tay, W.P., Leng, M.: Identifying infection sources and regions in large networks. IEEE Trans. Sig. Process. 61(11), 2850–2865 (2013)

    Article  MathSciNet  Google Scholar 

  25. Luo, W., Tay, W.P., Leng, M.: How to identify an infection source with limited observations, arXiv preprint arXiv:1309.4161

  26. Luo, W., Tay, W.P.: Finding an infection source under the sis model. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2930–2934. IEEE (2013)

    Google Scholar 

  27. Prakash, B.A., Vreeken, J., Faloutsos, C.: Spotting culprits in epidemics: how many and which ones? In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 11–20. IEEE (2012)

    Google Scholar 

  28. Lokhov, A.Y., Mézard, M., Ohta, H., Zdeborová, L.: Inferring the origin of an epidemy with dynamic message-passing algorithm, arXiv preprint arXiv:1303.5315

  29. Dong, W., Zhang, W., Tan, C.W.: Rooting out the rumor culprit from suspects, arXiv preprint arXiv:1301.6312

  30. Zang, W., Zhang, P., Zhou, C., Guo, L.: Discovering multiple diffusion source nodes in social networks. Procedia Comput. Sci. 29, 443–452 (2014)

    Article  Google Scholar 

  31. Zang, W., Zhang, P., Zhou, C., Guo, L.: Topic-aware source locating in social networks. In: 24th International World Wide Web Conference. ACM (2015)

    Google Scholar 

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Acknowledgments

This work was supported by the NSFC (No. 61370025), 863 projects (No. 2011AA01A103 and 2012AA012502), 973 project (No. 2013CB329606), and the Strategic Leading Science and Technology Projects of Chinese Academy of Sciences (No. XDA06030200), Australia ARC Discovery Project (DP1402206).

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Wenyu Zang, Qipeng Yao & Li Guo

  2. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, 100029, China

    Xiao Wang

  3. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qipeng Yao

Authors
  1. Wenyu Zang
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  2. Xiao Wang
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  3. Qipeng Yao
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  4. Li Guo
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Corresponding author

Correspondence to Xiao Wang .

Editor information

Editors and Affiliations

  1. University of Technology, Sydney, Australia

    Chengqi Zhang

  2. Xi'an Jiaotong University, Xi'an Jiaotong, China

    Wei Huang

  3. Chinese Academy of Sciences, Beijing, China

    Yong Shi

  4. University of Illinois Chicago, CHICAGO, Illinois, USA

    Philip S. Yu

  5. Fudan University, Shanghai, China

    Yangyong Zhu

  6. Research Center on Fictitious Econo, Chinese Academy of Sciences, Beijing, China

    Yingjie Tian

  7. University of Technology, Sydney, Australia

    Peng Zhang

  8. Victoria University, Melbourne, Victoria, Australia

    Jing He

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Zang, W., Wang, X., Yao, Q., Guo, L. (2015). A Fast Climbing Approach for Diffusion Source Inference in Large Social Networks. In: Zhang, C., et al. Data Science. ICDS 2015. Lecture Notes in Computer Science(), vol 9208. Springer, Cham. https://doi.org/10.1007/978-3-319-24474-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-24474-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24473-0

  • Online ISBN: 978-3-319-24474-7

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