MDER: modified degree with exclusion ratio algorithm for influence maximisation in social networks

Abstract

The online social network has become an integral part of our day to life and serves as an excellent platform for sharing ideas, opinions, and products. Influence maximization (IM) is a widely studied topic in the area of social network analysis. The objective of IM is to find influential nodes that can disseminate information to a larger extent in the network. Many local and global centrality measures are proposed to rank the nodes based on their spreading capability with certain limitations. Many proposed algorithms locate the spreaders sharing overlapping regions or are closely placed, which may cause interference in spreading. In this paper, based on the notion of maximum coverage of the information and minimum interference in spreading, we propose a novel semi-local algorithm named as modified degree centrality with exclusion ratio to identify influential nodes from diverse locations in the network. We use modified degree centrality by considering neighbours upto 2-hops and introduce the novel idea of exclusion ratio to ensure minimum overlapping between regions influenced by the chosen spreader nodes. Extensive experimental validation using classical information diffusion model demonstrates that the proposed method delivers better results in comparison to many popular contemporary methods of influence maximization.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. 1.

    Heidemann J, Klier M, Probst F (2012) Online social networks: a survey of a global phenomenon. Comput Netw 56(18):3866–3878

    Article  Google Scholar 

  2. 2.

    Krasnova H, Spiekermann S, Koroleva K, Hildebrand T (2010) Online social networks: why we disclose. J Inf Technol 25(2):109–125

    Article  Google Scholar 

  3. 3.

    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, pp 137–146

  4. 4.

    Li Y, Fan J, Wang Y, Tan KL (2018) Influence maximization on social graphs: a survey. IEEE Trans Knowl Data Eng 30(10):1852–72

    Article  Google Scholar 

  5. 5.

    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 57–66

  6. 6.

    Vega-Oliveros DA, da Fontoura CL, Rodrigues FA (2020) Influence maximization by rumor spreading on correlated networks through community identification. Commun Nonlinear Sci Numer Simul 83:105094

    MathSciNet  Article  Google Scholar 

  7. 7.

    Kumar S, Panda BS, Aggarwal D (2020) Community detection in complex networks using network embedding and gravitational search algorithm. J Intell Inf Syst. https://doi.org/10.1007/s10844-020-00625-6

  8. 8.

    Oueslati W, Arrami S, Dhouioui Z, Massaabi M (2021) Opinion leaders’ detection in dynamic social networks. Concurr Comput Pract Exp 33(1):e5692. https://doi.org/10.1002/cpe.5692

  9. 9.

    Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–39

    Article  Google Scholar 

  10. 10.

    Okamoto K, Chen W, Li XY (2008) Ranking of closeness centrality for large-scale social networks. In: International workshop on frontiers in algorithmics. Springer, Berlin, Heidelberg, pp 186–195

  11. 11.

    Bonacich P (2007) Some unique properties of eigenvector centrality. Soc Netw 29(4):555–64

    Article  Google Scholar 

  12. 12.

    Brin S, Page L (2012) Reprint of: The anatomy of a large-scale hypertextual web search engine. Comput Netw 56(18):3825–33

    Article  Google Scholar 

  13. 13.

    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, pp 509–518

  14. 14.

    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, pp 47–48

  15. 15.

    Huang H, Shen H, Meng Z (2020) Community-based influence maximization in attributed networks. Appl Intell 50(2):354–64

    Article  Google Scholar 

  16. 16.

    Kumar S, Singhla L, Jindal K, Grover K, Panda BS (2021) IM-ELPR: Influence maximization in social networks using label propagation based community structure. Appl Intell. https://doi.org/10.1007/s10489-021-02266-w

  17. 17.

    Satsuma J, Willox R, Ramani A, Grammaticos B, Carstea AS (2004) Extending the SIR epidemic model. Phys A Stat Mech Appl 336(3–4):369–75

    Article  Google Scholar 

  18. 18.

    Goldenberg J, Libai B, Muller E (2001) Using complex systems analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata. Acad Mark Sci Rev 9(3):1–8

    Google Scholar 

  19. 19.

    Murase Y, Jo HH, Török J, Kertész J, Kaski K (2019) Structural transition in social networks: the role of homophily. Sci Rep 9(1):1–8

    Article  Google Scholar 

  20. 20.

    Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 35–41. https://doi.org/10.2307/3033543

  21. 21.

    Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–93

    Article  Google Scholar 

  22. 22.

    Ma LL, Ma C, Zhang HF, Wang BH (2016) Identifying influential spreaders in complex networks based on gravity formula. Phys A Stat Mech Appl 451:205–12

    Article  Google Scholar 

  23. 23.

    Lü L, Zhou T, Zhang QM, Stanley HE (2016) The H-index of a network node and its relation to degree and coreness. Nat Commun 7:10168

    Article  Google Scholar 

  24. 24.

    Sheikhahmadi A, Nematbakhsh MA, Shokrollahi A (2015) Improving detection of influential nodes in complex networks. Phys A Stat Mech Appl 436:833–845

    Article  Google Scholar 

  25. 25.

    Berahmand K, Bouyer A, Samadi N (2019) A new local and multidimensional ranking measure to detect spreaders in social networks. Computing 101(11):1711–33

    MathSciNet  Article  Google Scholar 

  26. 26.

    Samadi N, Bouyer A (2019) Identifying influential spreaders based on edge ratio and neighborhood diversity measures in complex networks. Computing 101(8):1147–75

    MathSciNet  Article  Google Scholar 

  27. 27.

    Rui X, Yang X, Fan J, Wang Z (2020) A neighbour scale fixed approach for influence maximization in social networks. Computing. 102(2):427–449. https://doi.org/10.1007/s00607-019-00778-5

  28. 28.

    Kumar S, Saini M, Goel M, Panda BS (2021) Modeling information diffusion in online social networks using a modified forest-fire model. J Intell Inf Syst 56(2):355–377

    Article  Google Scholar 

  29. 29.

    Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42:599–653

    MathSciNet  Article  Google Scholar 

  30. 30.

    Watts DJ (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci 99(9):5766–71

    MathSciNet  Article  Google Scholar 

  31. 31.

    Yamasaki K, Matia K, Buldyrev SV, Fu D, Pammolli F, Riccaboni M, Stanley HE (2006) Preferential attachment and growth dynamics in complex systems. Phys Rev E 74(3):035103

    Article  Google Scholar 

  32. 32.

    Leskovec J, Krevl A, SNAP Datasets (2014) Stanford large network dataset collection, vol 2016, p 49. http://snap.stanford.edu/data

  33. 33.

    Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123

    MathSciNet  Article  Google Scholar 

  34. 34.

    Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data (TKDD) 1(1):2-es

    Article  Google Scholar 

  35. 35.

    Rozemberczki B, Allen C, Sarkar R (2019) Multi-scale attributed node embedding. arXiv preprint arXiv:1909.13021

  36. 36.

    Boguná M, Pastor-Satorras R, Díaz-Guilera A, Arenas A (2004) Models of social networks based on social distance attachment. Phys Rev E 70(5):056122

    Article  Google Scholar 

  37. 37.

    Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: friendship and mobility: user movement in location-based social networks. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD)

  38. 38.

    McAuley JJ, Leskovec J (2012) Learning to discover social circles in ego networks. In: NIPS, vol 2012, pp 548–556

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sanjay Kumar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Lohia, D., Pratap, D. et al. MDER: modified degree with exclusion ratio algorithm for influence maximisation in social networks. Computing (2021). https://doi.org/10.1007/s00607-021-00960-8

Download citation

Keywords

  • Influence maximization (IM)
  • Independent cascade (IC) model
  • Node centrality
  • Social networks (SNs)
  • SIR model
  • Viral marketing

Mathematics Subject Classification

  • 91D30