KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Neighborhood coreness algorithm for identifying a set of influential spreaders in complex networks

  • YANG, Xiong (School of Computer Science and Technology, Zhejiang University of Technology) ;
  • HUANG, De-Cai (School of Computer Science and Technology, Zhejiang University of Technology) ;
  • ZHANG, Zi-Ke (Alibaba Research Center for Complexity Sciences)
  • Received : 2017.01.09
  • Accepted : 2017.03.11
  • Published : 2017.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In recent years, there has been an increasing number of studies focused on identifying a set of spreaders to maximize the influence of spreading in complex networks. Although the k-core decomposition can effectively identify the single most influential spreader, selecting a group of nodes that has the largest k-core value as the seeds cannot increase the performance of the influence maximization because the propagation sphere of this group of nodes is overlapped. To overcome this limitation, we propose a neighborhood coreness cover and discount heuristic algorithm named "NCCDH" to identify a set of influential and decentralized seeds. Using this method, a node in the high-order shell with the largest neighborhood coreness and an uncovered status will be selected as the seed in each turn. In addition, the neighbors within the same shell layer of this seed will be covered, and the neighborhood coreness of the neighbors outside the shell layer will be discounted in the subsequent round. The experimental results show that with increases in the spreading probability, the NCCDH outperforms other algorithms in terms of the affected scale and spreading speed under the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models. Furthermore, this approach has a superior running time.

Keywords

References

  1. L Y Lu, D B Chen, X L Ren, Q M Zhang, Y C Zhang and T Zhou, "Vital nodes identification in complex networks," Physics Reports, vol.650, pp.1-63, September, 2016. https://doi.org/10.1016/j.physrep.2016.06.007
  2. H Li, J T Cui and J F Ma, "Social influence study in online networks: A three-level review," Journal of Computer Science and Technology, vol.30, no.1, pp.184-199, January, 2015. https://doi.org/10.1007/s11390-015-1512-7
  3. P.S.Romualdo and V.Alessandro, "Immunization of complex networks," Physical Review E, vol.65, no.3, pp.036104, April, 2002. https://doi.org/10.1103/PhysRevE.65.036104
  4. J. Leskovec, L.A. Adamic and B.A. Huberman, "The dynamics of viral marketing," ACM Transactions on the Web, vol.1,no.1, pp.1-39, April, 2007. https://doi.org/10.1145/1232722.1232723
  5. A.E.Motter, "Cascade control and defense in complex networks," Physical Review Letters, vol.93, no.9, pp.98701, September, 2004. https://doi.org/10.1103/PhysRevLett.93.098701
  6. W Chen, L.V.S. Lakshmanan and C. Castillo, "Information and influence propagation in social networks," Synthesis Lectures on Data Management, vol.5, no.4, pp.1-177, October, 2013. https://doi.org/10.2200/S00527ED1V01Y201308DTM037
  7. F. Radicchi, "Who is the best player ever? A complex network analysis of the history of professional tennis," PLoS ONE, vol.6, no.2, pp.e17249, February, 2011. https://doi.org/10.1371/journal.pone.0017249
  8. Y B Zhou, L Lu and M Li, "Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity," New Journal of Physics, vol.14, no.3, pp. 33033-33049, March, 2012. https://doi.org/10.1088/1367-2630/14/3/033033
  9. L C Freeman, "Centrality in social networks conceptual clarification," Social Networks, vol.1, no.3, pp.215-239, March, 1979. https://doi.org/10.1016/0378-8733(78)90021-7
  10. G Sabidussi, "The centrality index of a graph," Psychometrika, vol.31, no.4, pp.581-603, April, 1966. https://doi.org/10.1007/BF02289527
  11. L Katz, "A new status index derived from sociometric analysis," Psychometrika, vol.18, no.1, pp.39-43, March, 1953. https://doi.org/10.1007/BF02289026
  12. M Kitsak, L K Gallos and S Havlin, "Identification of influential spreaders in complex networks," Nature Physics, vol.6, no.11, pp.888-893, August, 2010. https://doi.org/10.1038/nphys1746
  13. A Zeng and C J Zhang, "Ranking spreaders by decomposing complex networks," Physics Letters A, vol.377, no.14, pp.1031-1035, June, 2013. https://doi.org/10.1016/j.physleta.2013.02.039
  14. J G Liu, Z M Ren and Q Guo, "Ranking the spreading influence in complex networks," Physica A, vol.392, no.18, pp.4154-4159, September, 2013. https://doi.org/10.1016/j.physa.2013.04.037
  15. S Pei, L Muchnik, J S Andrade, Z Zheng and H A Makse, "Searching for superspreaders of information in real-world social media," Scientific Reports, vol.4, pp.5547, July, 2014.
  16. J Bae and S Kim, "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A, vol.395, no.4, pp.549-559, February, 2014. https://doi.org/10.1016/j.physa.2013.10.047
  17. Y Liu, M Tang, T Zhou and Y Do, "Improving the accuracy of the k-shell method by removing redundant links: from a perspective of spreading dynamics," Scientific Reports, vol.5, pp.13172, May, 2015. https://doi.org/10.1038/srep13172
  18. Y Liu, M Tang, T Zhou and Y Do, "Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition," Scientific Reports. vol.5, pp.9602, May, 2015. https://doi.org/10.1038/srep09602
  19. Y H Fu, C Y Huang and C T Sun, "Using global diversity and local topology features to identify influential network spreaders," Physica A, vol.433, no.9, pp.344-355, September, 2015. https://doi.org/10.1016/j.physa.2015.03.042
  20. PGV Naranjo, M Shojafar, H Mostafaei ,Z Pooranian and E Baccarelli, "P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks," Journal of Supercomputing, vol.73, no.2, pp.733-755, February, 2017. https://doi.org/10.1007/s11227-016-1785-9
  21. Z Pooranian, M Shojafar, JH Abawajy and A Abraham, "An efficient meta-heuristic algorithm for grid computing," Journal of Combinatorial Optimization, vol.30, no.3, pp.413-434, October, 2015. https://doi.org/10.1007/s10878-013-9644-6
  22. Z Pooranian, N Nikmehr, S Najafi-Ravadanegh, H Mahdin and J Abawajy, "Economical and Environmental Operation of Smart Networked Microgrids under Uncertainties Using NSGA-II," in Proc. of the 24th International Conference on Software, Telecommunications and Computer Networks, pp.1-7, September 22 - 24, 2016.
  23. D Kempe, J Kleinberg and E Tardos, "Maximizing the spread of influence through a social network." in Proc. of the 9th ACM Conference on Knowledge Discovery and Data Mining, pp.137-146, August 24 - 27, 2003.
  24. Z K Zhang, C Liu, X X Zhan, X Lu, C X Zhang, Y C Zhang, "Dynamics of information diffusion and its applications on complex networks," Physics Reports, vol.651, pp.1-34, September, 2016. https://doi.org/10.1016/j.physrep.2016.07.002
  25. J Leskovec, A Krause, C Guestrin, C Faloutsos, J VanBriesen, and N Glance, "Cost-effective outbreak detection in networks," in Proc. of the 13th ACM Conference on Knowledge Discovery and Data Mining, pp.420-429, August 12 - 15, 2007.
  26. W Chen, Y Wang and S Yang, "Efficient influence maximization in social networks," in Proc. of the 15th ACM Conference on Knowledge Discovery and Data Mining, pp.199-208, June 28 - July 01, 2009.
  27. P SanKar, S Kundu, and CA Murthy, "Centrality Measures, Upper Bound, and Influence Maximization in Large Scale Directed Social Networks," Fundamenta Informaticae, vol.130, no.3, pp.317-342, July, 2014.
  28. H Kim, K Beznosov and E Yoneki, "A study on the influential neighbors to maximize information diffusion in online social networks," Computational Social Networks, vol.2, no.1, pp.1-15, February, 2015. https://doi.org/10.1186/s40649-014-0008-x
  29. Y Liu, M Tang and T Zhou, "Identify influential spreaders in complex networks, the role of neighborhood," Physica A, vol.452, no.6, pp.289-298, June, 2016. https://doi.org/10.1016/j.physa.2016.02.028
  30. J X Zhang, D B Chen, Q Dong and Z D Zhao, "Identifying a set of influential spreaders in complex networks," Scientific Reports, vol.6, pp.27823, June, 2016. https://doi.org/10.1038/srep27823
  31. J X Cao, D Dong, X Shun, X Zheng, B Liu and J Z Luo, "A k-core based Algorithm for Influence Maximization in Social Networks," Chinese Journal of Computers, vol.38, no.2, pp.238-248, February, 2015.
  32. R A Rossi and N K Ahmed, "An Interactive Data Repository with Visual Analytics," ACM SIGKDD Explorations Newsletter, vol.17, no.2, pp.37-41, February, 2016. https://doi.org/10.1145/2897350.2897355
  33. J Leskovec, J Kleinberg and C Faloutsos, "Graph Evolution: Densification and Shrinking Diameters," ACM Transactions on Knowledge Discovery from Data,vol.1, no.1, pp.1-41, March, 2007. https://doi.org/10.1145/1217299.1217300
  34. M E Newman, "The structure of scientific collaboration networks," in Proc. of the National Academy of Sciences, vol.98, no.2, pp.404-409, February, 2001. https://doi.org/10.1073/pnas.98.2.404