Applied Intelligence

, Volume 49, Issue 7, pp 2684–2698 | Cite as

A reversed node ranking approach for influence maximization in social networks

  • Xiaobin Rui
  • Fanrong MengEmail author
  • Zhixiao WangEmail author
  • Guan Yuan


Influence maximization, i.e. to maximize the influence spread in a social network by finding a group of influential nodes as small as possible, has been studied widely in recent years. Many methods have been developed based on either explicit Monte Carlo simulation or scoring systems, among which the former perform well yet are very time-consuming while the latter ones are efficient but sensitive to different spreading models. In this paper, we propose a novel influence maximization algorithm in social networks, named Reversed Node Ranking (RNR). It exploits the reversed rank information of a node and the effects of its neighbours upon this node to estimate its influence power, and then iteratively selects the top node as a seed node once the ranking reaches stable. Besides, we also present two optimization strategies to tackle the rich-club phenomenon. Experiments on both Independent Cascade (IC) model and Weighted Cascade (WC) model show that our proposed RNR method exhibits excellent performance and outperforms other state-of-the-arts. As a by-product, our work also reveals that the IC model is more sensitive to the rich-club phenomenon than the WC model.


Influence maximization Social network Reversed rank Spreading model 



This work was supported by Outstanding Innovation Scholarship for Doctoral Candidate of ”Double First Rate” Construction Disciplines of CUMT.


  1. 1.
    Arora A, Galhotra S, Ranu S (2017) Debunking the myths of influence maximization: an in-depth benchmarking study. In: Proceedings of the 2017 ACM international conference on management of data. ACM, pp 651–666Google Scholar
  2. 2.
    Bavelas A (1950) Communication patterns in task-oriented groups. J Acoustic Soc Amer 22(6):725–730CrossRefGoogle Scholar
  3. 3.
    Chen W, Wang C, Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 1029–1038Google Scholar
  4. 4.
    Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 199–208Google Scholar
  5. 5.
    Cheng S, Shen H, Huang J, Zhang G, Cheng X (2013) Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: Proceedings of the 22nd ACM international conference on information & knowledge management. ACM, pp 509–518Google Scholar
  6. 6.
    Colizza V, Flammini A, Serrano MA, Vespignani A (2006) Detecting rich-club ordering in complex networks. Nature Phys 2(2):110CrossRefGoogle Scholar
  7. 7.
    Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 57–66Google Scholar
  8. 8.
    Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41CrossRefGoogle Scholar
  9. 9.
    Goyal A, Lu W, Lakshmanan LV (2011) Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th international conference companion on World Wide Web. ACM, pp 47–48Google Scholar
  10. 10.
    Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 137–146Google Scholar
  11. 11.
    Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (TKDD) 1(1):2CrossRefGoogle Scholar
  12. 12.
    Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N. (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 420–429Google Scholar
  13. 13.
    Liu D, Jing Y, Zhao J, Wang W, Song G (2017) A fast and efficient algorithm for mining top-k nodes in complex networks. Sci Rep 7:43330CrossRefGoogle Scholar
  14. 14.
    Miller G, Fellbaum C (1998) Wordnet: an electronic lexical database. MIT Press, CambridgezbMATHGoogle Scholar
  15. 15.
    Newman ME (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74((3 Pt 2)):036104MathSciNetCrossRefGoogle Scholar
  16. 16.
    Nguyen DL, Nguyen TH, Do TH, Yoo M (2017) Probability-based multi-hop diffusion method for influence maximization in social networks. Wirel Pers Commun 93(4):903–916CrossRefGoogle Scholar
  17. 17.
    Page L (1998) The pagerank citation ranking: bringing order to the web. Stanford Digital Libraries Working Paper 9(1):1–14Google Scholar
  18. 18.
    Peng S, Zhou Y, Cao L, Yu S, Niu J, Jia W (2018) Influence analysis in social networks: a survey. J Netw Comput Appl 106:17–32CrossRefGoogle Scholar
  19. 19.
    Radicchi F, Castellano C (2017) Fundamental difference between superblockers and superspreaders in networks. Phys Rev E 95(1):012318CrossRefGoogle Scholar
  20. 20.
    Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 61–70Google Scholar
  21. 21.
    Ripeanu M, Foster I, Iamnitchi A (2002) Mapping the gnutella network: properties of large-scale peer-to-peer systems and implications for system design. Comput Sci 6:2002Google Scholar
  22. 22.
    Wang X, Su Y, Zhao C, Yi D (2016) Effective identification of multiple influential spreaders by degreepunishment. Phys A: Stat Mech Appl 461:238–247CrossRefGoogle Scholar
  23. 23.
    Zhou S, Mondragón RJ (2004) The rich-club phenomenon in the internet topology. IEEE Commun Lett 8(3):180–182CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyChina University of Mining and TechnologyXuzhouChina

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