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A survey on meta-heuristic algorithms for the influence maximization problem in the social networks

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Abstract

The different communications of users in social networks play a key role in effect to each other. The effect is important when they can achieve their goals through different communications. Studying the effect of specific users on other users has been modeled on the influence maximization problem on social networks. To solve this problem, different algorithms have been proposed that each of which has attempted to improve the influence spread and running time than other algorithms. Due to the lack of a review of the meta-heuristic algorithms for the influence maximization problem so far, in this paper, we first perform a comprehensive categorize of the presented algorithms for this problem. Then according to the efficient results and significant progress of the meta-heuristic algorithms over the last few years, we describe the comparison, advantages, and disadvantages of these algorithms.

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References

  1. 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

  2. Aghaee Z, Kianian S (2020) Efficient influence spread estimation for influence maximization. Soc Netw Anal Min 10(1):1–21

    Article  Google Scholar 

  3. Beni HA et al (2020) IMT: selection of top-k nodes based on the topology structure in social networks. In: 2020 6th international conference on web research (ICWR). IEEE

  4. Aghaee Z et al (2020) A heuristic algorithm focusing on the rich-club phenomenon for the influence maximization problem in social networks. In: 2020 6th International Conference on Web Research (ICWR). IEEE

  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

  6. Li W et al (2020) Three-hop velocity attenuation propagation model for influence maximization in social networks. World Wide Web 23(2):1261–1273

    Article  Google Scholar 

  7. Sumith N, Annappa B, Bhattacharya S (2018) Influence maximization in large social networks: Heuristics, models and parameters. Futur Gener Comput Syst 89:777–790

    Article  Google Scholar 

  8. Sanatkar MR (2020) The dynamics of polarized beliefs in networks governed by viral diffusion and media influence. Soc Netw Anal Min 10(1):1–21

    Article  Google Scholar 

  9. Saxena B, Kumar P (2019) A node activity and connectivity-based model for influence maximization in social networks. Soc Netw Anal Min 9(1):40

    Article  Google Scholar 

  10. Peng S et al (2018) Influence analysis in social networks: A survey. J Netw Comput Appl 106:17–32

    Article  Google Scholar 

  11. Tang J, Tang X, Yuan J (2018) An efficient and effective hop-based approach for influence maximization in social networks. Soc Netw Anal Min 8(1):10

    Article  Google Scholar 

  12. Chang B et al (2018) Study on information diffusion analysis in social networks and its applications. Int J Autom Comput 15(4):377–401

    Article  Google Scholar 

  13. Banerjee S, Jenamani M, Pratihar DK (2018) A survey on influence maximization in a social network. arXiv preprint arXiv:1808.05502

  14. Sun J, Tang J (2011) A survey of models and algorithms for social influence analysis. Social network data analytics. Springer, pp 177–214

    Chapter  Google Scholar 

  15. Li M et al (2017) A survey on information diffusion in online social networks: Models and methods. Information 8(4):118

    Article  Google Scholar 

  16. Pei S, Makse HA (2013) Spreading dynamics in complex networks. J Stat Mech: Theory Exp 2013(12):P12002

    Article  Google Scholar 

  17. Pastor-Satorras R et al (2015) Epidemic processes in complex networks. Rev Mod Phys 87(3):925

    Article  MathSciNet  Google Scholar 

  18. Samadi N, Bouyer A (2019) Identifying influential spreaders based on edge ratio and neighborhood diversity measures in complex networks. Computing 101(8):1147–1175

    Article  MathSciNet  Google Scholar 

  19. Zafarani R et al (2014) Information diffusion in social media. Social Media mining: an introduction. NP, Cambridge UP

  20. Rui X et al (2019) A reversed node ranking approach for influence maximization in social networks. Appl Intell 49(7):2684–2698

    Article  Google Scholar 

  21. Singh SS et al (2020) IM-SSO: Maximizing influence in social networks using social spider optimization. Concurr Comput Pract Exp 32(2):e5421

    Google Scholar 

  22. Shang J et al (2017) CoFIM: a community-based framework for influence maximization on large-scale networks. Knowl-Based Syst 117:88–100

    Article  Google Scholar 

  23. Leskovec J et al (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

  24. 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

  25. 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

  26. Cheng S et al (2013) Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: Proceedings of the 22nd ACM international conference on Information and knowledge management

  27. Heidari M, Asadpour M, Faili H (2015) SMG: Fast scalable greedy algorithm for influence maximization in social networks. Phys A 420:124–133

    Article  Google Scholar 

  28. Zhang J-X et al (2016) Identifying a set of influential spreaders in complex networks. Sci Rep 6:27823

    Article  Google Scholar 

  29. Berahmand K, Bouyer A, Samadi N (2019) A new local and multidimensional ranking measure to detect spreaders in social networks. Computing 101(11):1711–1733

    Article  MathSciNet  Google Scholar 

  30. Liu D et al (2017) A fast and efficient algorithm for mining top-k nodes in complex networks. Sci Rep 7:43330

    Article  Google Scholar 

  31. Luo Z-L et al (2012) A pagerank-based heuristic algorithm for influence maximization in the social network. Recent progress in data engineering and internet technology. Springer, pp 485–490

    Chapter  Google Scholar 

  32. Ahajjam S, Badir H (2018) Identification of influential spreaders in complex networks using HybridRank algorithm. Sci Rep 8(1):1–10

    Article  Google Scholar 

  33. Qiu L et al (2019) LGIM: A global selection algorithm based on local influence for influence maximization in social networks. IEEE Access

  34. Tang Y, Xiao X, Shi Y (2014) Influence maximization: Near-optimal time complexity meets practical efficiency. In: Proceedings of the 2014 ACM SIGMOD international conference on Management of data

  35. Tang Y, Shi Y, Xiao X (2015) Influence maximization in near-linear time: A martingale approach. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data

  36. Aghaee Z, Kianian S (2020) Influence maximization algorithm based on reducing search space in the social networks. SN Appl Sci 2(12):1–14

    Article  Google Scholar 

  37. Wang Y et al (2010) Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

  38. Zhao Y, Li S, Jin F (2016) Identification of influential nodes in social networks with community structure based on label propagation. Neurocomputing 210:34–44

    Article  Google Scholar 

  39. Qiu L et al (2019) PHG: a three-phase algorithm for influence maximization based on community structure. IEEE Access 7:62511–62522

    Article  Google Scholar 

  40. Singh SS et al (2019) C2IM: community based context-aware influence maximization in social networks. Phys A 514:796–818

    Article  MathSciNet  Google Scholar 

  41. Bouyer A, Ahmadi H (2018) A new greedy method based on cascade model for the influence maximization problem in social networks. J Inf Commun Technol 10(37):85–100

    Google Scholar 

  42. Banerjee S, Jenamani M, Pratihar DK (2019) ComBIM: a community-based solution approach for the Budgeted Influence Maximization Problem. Expert Syst Appl 125:1–13

    Article  Google Scholar 

  43. Beni HA, Bouyer A (2020) TI‑SC: top‑k influential nodes selection based on community detection and scoring criteria in social networks

  44. Kimura M, Saito K (2006) Tractable models for information diffusion in social networks. In: European conference on principles of data mining and knowledge discovery. Springer, Berlin

  45. Chen W, Yuan Y, Zhang L (2010) Scalable influence maximization in social networks under the linear threshold model. In: 2010 IEEE international conference on data mining. IEEE

  46. Chen W, Wang C, Wang Y (2010) 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

  47. Goyal A, Lu W, Lakshmanan LV (2011) Simpath: An efficient algorithm for influence maximization under the linear threshold model. In: 2011 IEEE 11th international conference on data mining. IEEE

  48. Wang C, Chen W, Wang Y (2012) Scalable influence maximization for independent cascade model in large-scale social networks. Data Min Knowl Disc 25(3):545–576

    Article  MathSciNet  Google Scholar 

  49. Kim J, Kim S-K, 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). IEEE

  50. Jiang Q et al (2011) Simulated annealing based influence maximization in social networks. In: Twenty-fifth AAAI conference on artificial intelligence

  51. Tsai C-W, Yang Y-C, Chiang M-C (2015) A genetic newgreedy algorithm for influence maximization in social network. In: 2015 IEEE international conference on systems, man, and cybernetics. IEEE

  52. Gong M et al (2016) Influence maximization in social networks based on discrete particle swarm optimization. Inf Sci 367:600–614

    Article  Google Scholar 

  53. Cui L et al (2018) DDSE: a novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks. J Netw Comput Appl 103:119–130

    Article  Google Scholar 

  54. Tang J et al (2018) Maximizing the spread of influence via the collective intelligence of discrete bat algorithm. Knowl-Based Syst 160:88–103

    Article  Google Scholar 

  55. Tang J et al (2019) Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization. Phys A 513:477–496

    Article  Google Scholar 

  56. Tang J et al (2019) An adaptive discrete particle swarm optimization for influence maximization based on network community structure. Int J Modern Phys C (IJMPC) 30(06):1–21

    MathSciNet  Google Scholar 

  57. Ma L, Liu Y (2019) Maximizing three-hop influence spread in social networks using discrete comprehensive learning artificial bee colony optimizer. Appl Soft Comput 83:105606

    Article  Google Scholar 

  58. Zareie A, Sheikhahmadi A, Jalili M (2020) Identification of influential users in social network using gray wolf optimization algorithm. Expert Syst Appl 142:112971

    Article  Google Scholar 

  59. Tang J et al (2020) A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks. Knowl-Based Syst 187:104833

    Article  Google Scholar 

  60. Krömer P, Nowaková J (2017) Guided genetic algorithm for the influence maximization problem. In: international computing and combinatorics conference. Springer, Berlin

  61. Weskida M, Michalski R (2016) Evolutionary algorithm for seed selection in social influence process. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE

  62. da Silva AR et al (2018) Influence maximization in network by genetic algorithm on linear threshold model. In: international conference on computational science and its applications. Springer, Berlin

  63. Bucur D, Iacca G (2016) Influence maximization in social networks with genetic algorithms. In: European conference on the applications of evolutionary computation. Springer, Berlin

  64. Bouyer A, Roghani H (2020) LSMD: a fast and robust local community detection starting from low degree nodes in social networks. Futur Gener Comput Syst 113:41–57

    Article  Google Scholar 

  65. Taheri S, Bouyer A (2020) Community detection in social networks using affinity propagation with adaptive similarity matrix. Big Data 8(3):189–202

    Article  Google Scholar 

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Correspondence to Zahra Aghaee.

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Aghaee, Z., Ghasemi, M.M., Beni, H.A. et al. A survey on meta-heuristic algorithms for the influence maximization problem in the social networks. Computing 103, 2437–2477 (2021). https://doi.org/10.1007/s00607-021-00945-7

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