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ACO-IM: maximizing influence in social networks using ant colony optimization

  • Shashank Sheshar SinghEmail author
  • Kuldeep Singh
  • Ajay Kumar
  • Bhaskar Biswas
Methodologies and Application
  • 44 Downloads

Abstract

Online social networks play an essential role in propagating information, innovation, and ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a fundamental problem, known as influence maximization (IM) or subset selection problem. The IM problem aims to identify a small subset of users, viz. seed nodes such that overall influence spread can be maximized. The seed selection problem is NP-hard, unfortunately. A greedy solution of IM problem is not sufficient due to the use of time-consuming Monte Carlo simulations, which is limited to small-scale networks. However, the greedy solution ensures a good approximation guarantee. In this paper, a local influence evaluation heuristic is adopted to approximate local influence within the two-hope area. With this heuristic, an expected diffusion value under the traditional diffusion models is evaluated. To optimize local influence evaluation heuristic, an influence maximization algorithm based on ant colony optimization (ACO-IM) is presented. ACO-IM redefines the representation and updates the rule of pheromone deposited by ants and heuristic information. The algorithm uses the probabilistic environment to avoid premature convergence. Finally, the experimental results show the superiority of the proposed algorithm. The statistical tests are also performed to distinguish the proposed method from the state-of-the-art methods.

Keywords

Information diffusion Influence maximization Social networks Ant colony optimization 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Human or animal rights

The article does not contain any studies with human or animal subjects. This article presents an algorithm ACO-IM.

References

  1. Brown JJ, Reingen PH (1987) Social ties and word-of-mouth referralbehavior. J Consum Res 14(3):350–362.  https://doi.org/10.1086/209118 CrossRefGoogle Scholar
  2. 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, KDD ’09. ACM, New York, pp 199–208.  https://doi.org/10.1145/1557019.1557047
  3. Chen W, Wang C, Wang Y (2010a) 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, KDD ’10. ACM, New York, pp 1029–1038.  https://doi.org/10.1145/1835804.1835934
  4. Chen W, Yuan Y, Zhang L (2010b) Scalable influence maximization in social networks under the linear threshold model. In: Proceedings of the 2010 IEEE international conference on data mining, ICDM ’10, IEEE Computer Society, Washington, DC, pp 88–97.  https://doi.org/10.1109/ICDM.2010.118
  5. Chen W, Lakshmanan LVS, Castillo C (2013) Information and influence propagation in social networks. Morgan & Claypool Publishers, San RafaelCrossRefGoogle Scholar
  6. Chen Y-C, Zhu W-Y, Peng W-C, Lee W-C, Lee S-Y (2014) Cim: community-based influence maximization in social networks. ACM Trans Intell Syst Technol 5(2):25:1–25:31.  https://doi.org/10.1145/2532549 CrossRefGoogle Scholar
  7. Chen S, Fan J, Li G, Feng J, Tan K-L, Tang J (2015) Online topic-aware influence maximization. Proc VLDB Endow 8(6):666–677.  https://doi.org/10.14778/2735703.2735706 CrossRefGoogle Scholar
  8. 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, CIKM ’13. ACM, New York, pp 509–518.  https://doi.org/10.1145/2505515.2505541
  9. Christakis NA, Fowler JH (2009) Connected: the surprising power of our social networks and how they shape our lives. Little, BrownGoogle Scholar
  10. Derrac J, Garcá S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18.  https://doi.org/10.1016/j.swevo.2011.02.002 CrossRefGoogle Scholar
  11. 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, KDD ’01. ACM, New York, pp 57–66.  https://doi.org/10.1145/502512.502525
  12. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2):243–278.  https://doi.org/10.1016/j.tcs.2005.05.020 MathSciNetCrossRefzbMATHGoogle Scholar
  13. Dorigo M, Stützle T (2003) The ant colony optimization metaheuristic: algorithms, applications, and advances. Springer, Boston, pp 250–285.  https://doi.org/10.1007/0-306-48056-5_9 CrossRefzbMATHGoogle Scholar
  14. Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2, pp 1470–1477.  https://doi.org/10.1109/CEC.1999.782657
  15. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701.  https://doi.org/10.1080/01621459.1937.10503522 CrossRefzbMATHGoogle Scholar
  16. Ge H, Huang J, Di C, Li J, Li S (2017) Learning automata based approach for influence maximization problem on social networks. IEEE Second Int Conf Data Sci Cyberspace 2017:108–117.  https://doi.org/10.1109/DSC.2017.54 CrossRefGoogle Scholar
  17. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Market Lett 12(3):211–223.  https://doi.org/10.1023/A:1011122126881 CrossRefGoogle Scholar
  18. Gomez Rodriguez M, Schölkopf B (2012) Influence maximization in continuous time diffusion networks. In: Proceedings of the 29th international conference on machine learning. Omnipress, New York, pp 313–320Google Scholar
  19. Gomez-Rodriguez M, Song L, Du N, Zha H, Schölkopf B (2016) Influence estimation and maximization in continuous-time diffusion networks. ACM Trans Inf Syst 34(2):9:1–9:33.  https://doi.org/10.1145/2824253 CrossRefGoogle Scholar
  20. Gong M, Yan J, Shen B, Ma L, Cai Q (2016) Influence maximization in social networks based on discrete particle swarm optimization. Inf Sci 367–368:600–614.  https://doi.org/10.1016/j.ins.2016.07.012 CrossRefGoogle Scholar
  21. 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, WWW ’11, ACM, New York, pp 47–48.  https://doi.org/10.1145/1963192.1963217
  22. Goyal A, Lu W, Lakshmanan LVS (2011) Simpath: an efficient algorithm for influence maximization under the linear threshold model. In: Proceedings of the 2011 IEEE 11th international conference on data mining, ICDM ’11. IEEE Computer Society, Washington, DC, pp 211–220.  https://doi.org/10.1109/ICDM.2011.132
  23. Guo L, Zhang D, Cong G, Wu W, Tan KL (2017) Influence maximization in trajectory databases. IEEE Trans Knowl Data Eng 29(3):627–641.  https://doi.org/10.1109/TKDE.2016.2621038 CrossRefGoogle Scholar
  24. Hu L, Yuan X, Liu X, Xiong S, Luo X (2018) Efficiently detecting protein complexes from protein interaction networks via alternating direction method of multipliers. IEEE ACM Trans Comput Biol Bioinf.  https://doi.org/10.1109/TCBB.2018.2844256 CrossRefGoogle Scholar
  25. Hu L, Hu P, Yuan X, Luo X, You Z (2019) Incorporating the coevolving information of substrates in predicting hiv-1 protease cleavage sites. IEEE ACM Trans Comput Biol Bioinform.  https://doi.org/10.1109/TCBB.2019.2914208 CrossRefGoogle Scholar
  26. Isella L, Stehlé J, Barrat A, Cattuto C, Pinton J-F, den Broeck WV (2011) What’s in a crowd? analysis of face-to-face behavioral networks. J Theor Biol 271(1):166–180.  https://doi.org/10.1016/j.jtbi.2010.11.033 MathSciNetCrossRefzbMATHGoogle Scholar
  27. Jiang Q, Song G, Cong G, Wang Y, Si W, Xie K (2011) Simulated annealing based influence maximization in social networks. In: Proceedings of the twenty-fifth AAAI conference on artificial intelligence, AAAI’11. AAAI Press, pp 127–132. http://dl.acm.org/citation.cfm?id=2900423.2900443
  28. Jung K, Heo W, Chen W (2012) Irie: scalable and robust influence maximization in social networks. In: Proceedings of the 2012 IEEE 12th international conference on data mining, ICDM ’12. IEEE Computer Society, Washington, DC, pp 918–923.  https://doi.org/10.1109/ICDM.2012.79
  29. Kempe D, Kleinberg J, Tardos E (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, KDD ’03. ACM, New York, pp 137–146.  https://doi.org/10.1145/956750.956769
  30. Kim J, Kim SK, Yu H (2013) Scalable and parallelizable processing of influence maximization for large-scale social networks? In: 2013 IEEE 29th international conference on data engineering (ICDE), pp 266–277.  https://doi.org/10.1109/ICDE.2013.6544831
  31. Kimura M, Saito K (2006) Tractable models for information diffusion in social networks. In: Fürnkranz J, Scheffer T, Spiliopoulou M (eds) Knowledge discovery in databases: PKDD 2006. Springer, Berlin, pp 259–271CrossRefGoogle Scholar
  32. Kundu S, Murthy CA, Pal SK (2011) A new centrality measure for influence maximization in social networks. In: Kuznetsov SO, Mandal DP, Kundu MK, Pal SK (eds) Pattern recognition and machine intelligence. Springer, Berlin, pp 242–247CrossRefGoogle Scholar
  33. Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data 10(1145/1217299):1217301Google Scholar
  34. Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web 10(1145/1232722):1232727Google Scholar
  35. 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, KDD ’07. ACM, New York, pp 420–429.  https://doi.org/10.1145/1281192.1281239
  36. Li Y, Zhang D, Tan K-L (2015) Real-time targeted influence maximization for online advertisements. Proc VLDB Endow 8(10):1070–1081.  https://doi.org/10.14778/2794367.2794376 CrossRefGoogle Scholar
  37. Li G, Chen S, Feng J, Tan K-l, Li W-s (2014) Efficient location-aware influence maximization. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data, SIGMOD ’14. ACM, New York, pp 87–98.  https://doi.org/10.1145/2588555.2588561
  38. Li X, Smith JD, Dinh TN, Thai MT (2019) Tiptop: (almost) exact solutions for influence maximization in billion-scale networks. IEEE ACM Trans Netw 27(2):649–661.  https://doi.org/10.1109/TNET.2019.2898413 CrossRefGoogle Scholar
  39. Lin S-C, Lin S-D, Chen M-S (2015) A learning-based framework to handle multi-round multi-party influence maximization on social networks. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’15. ACM, New York, pp 695–704.  https://doi.org/10.1145/2783258.2783392
  40. Luo X, Zhou M, Xia Y, Zhu Q, Ammari AC, Alabdulwahab A (2016) Generating highly accurate predictions for missing qos data via aggregating nonnegative latent factor models. IEEE Trans Neural Netw Learn Syst 27(3):524–537.  https://doi.org/10.1109/TNNLS.2015.2412037 MathSciNetCrossRefGoogle Scholar
  41. Luo X, Sun J, Wang Z, Li S, Shang M (2017) Symmetric and nonnegative latent factor models for undirected, high-dimensional, and sparse networks in industrial applications. IEEE Trans Ind Inf 13(6):3098–3107.  https://doi.org/10.1109/TII.2017.2724769 CrossRefGoogle Scholar
  42. Luo X, Zhou M, Li S, Hu L, Shang M (2019) Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications. IEEE Trans Cybern.  https://doi.org/10.1109/TCYB.2019.2894283 CrossRefGoogle Scholar
  43. Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev 74:036104MathSciNetGoogle Scholar
  44. Nguyen HT, Cano A, Tam V, Dinh TN (2019) Blocking self-avoiding walks stops cyber-epidemics: a scalable gpu-based approach. IEEE Trans Knowl Data Eng.  https://doi.org/10.1109/TKDE.2019.2904969 CrossRefGoogle Scholar
  45. Ohsaka N, Akiba T, Yoshida Y, Kawarabayashi K-I (2014) Fast and accurate influence maximization on large networks with pruned monte-carlo simulations. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence, AAAI’14. AAAI Press, pp 138–144. http://dl.acm.org/citation.cfm?id=2893873.2893897
  46. Pei S, Muchnik L, Andrade JS Jr, Zheng Z, Makse HA (2014) Searching for superspreaders of information in real-world social media. Sci Rep 4:5547CrossRefGoogle Scholar
  47. Ripeanu M, Iamnitchi A, Foster I (2002) Mapping the gnutella network. IEEE Internet Comput 6(1):50–57.  https://doi.org/10.1109/4236.978369 CrossRefGoogle Scholar
  48. Singh SS, Kumar A, Singh K, Biswas B (2019a) Im-sso: maximizing influence in social networks using social spider optimization. Concurr Comput Pract Exp, e5421, e5421 cpe.5421. https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5421
  49. Singh SS, Kumar A, Singh K, Biswas B (2019b) Lapso-im: a learning-based influence maximization approach for social networks. Appl Soft Comput 82:105554.  https://doi.org/10.1016/j.asoc.2019.105554 CrossRefGoogle Scholar
  50. Singh SS, Singh K, Kumar A, Biswas B (2019c) Coim: community-based influence maximization in social networks. In: Luhach AK, Singh D, Hsiung P-A, Hawari KBG, Lingras P, Singh PK (eds) Advanced informatics for computing research. Springer, Singapore, pp 440–453CrossRefGoogle Scholar
  51. Singh SS, Kumar A, Singh K, Biswas B (2019d) C2IM: community based context-aware influence maximization in social networks. Phys A Stat Mech Appl 514:796–818.  https://doi.org/10.1016/j.physa.2018.09.142 MathSciNetCrossRefGoogle Scholar
  52. Singh SS, Singh K, Kumar A, Biswas B (2019e) Mim2: multiple influence maximization across multiple social networks. Phys A Stat Mech Appl 526:120902.  https://doi.org/10.1016/j.physa.2019.04.138 CrossRefGoogle Scholar
  53. Sviridenko M (2004) A note on maximizing a submodular set function subject to a knapsack constraint. Oper Res Lett 32(1):41–43.  https://doi.org/10.1016/S0167-6377(03)00062-2 MathSciNetCrossRefzbMATHGoogle Scholar
  54. Tang Y, Xiao X, Shi Y (2014) Influence maximization: near-optimal time complexity meets practical efficiency. In: SIGMOD conferenceGoogle Scholar
  55. Teng Y-W, Tai C-H, Yu PS, Chen M-S (2018) Revenue Maximization on the multi-grade product, pp 576–584.  https://doi.org/10.1137/1.9781611975321.65 CrossRefGoogle Scholar
  56. Wang Y, Feng X (2009) A potential-based node selection strategy for influence maximization in a social network. In: Huang R, Yang Q, Pei J, Gama J, Meng X, Li X (eds) Adv Data Min Appl. Springer, Berlin, pp 350–361CrossRefGoogle Scholar
  57. Wang Y, Cong G, Song G, Xie K (2010) Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: KDDGoogle Scholar
  58. Wu P, Pan L (2017) Scalable influence blocking maximization in social networks under competitive independent cascade models. Comput Netw 123:38–50.  https://doi.org/10.1016/j.comnet.2017.05.004 CrossRefGoogle Scholar
  59. Ye M, Liu X, Lee W-C (2012) Exploring social influence for recommendation: a generative model approach. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’12. ACM, New York, pp 671–680.  https://doi.org/10.1145/2348283.2348373
  60. Zhu Y, Li D, Zhang Z (2016) Minimum cost seed set for competitive social influence. In: IEEE INFOCOM 2016-The 35th annual IEEE international conference on computer communications, pp 1–9.  https://doi.org/10.1109/INFOCOM.2016.7524472
  61. Zlochin M, Birattari M, Meuleau N, Dorigo M (2004) Model-based search for combinatorial optimization: a critical survey. Ann Oper Res 131(1):373–395.  https://doi.org/10.1023/B:ANOR.0000039526.52305.af MathSciNetCrossRefzbMATHGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (BHU) VaranasiVaranasiIndia

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