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Influence maximization in mobile social networks based on RWP-CELF

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Abstract

Influence maximization (IM) problem for messages propagation is an important topic in mobile social networks. The success of the spreading process depends on the mechanism for selection of the influential user. Beside selection of influential users, the computation and running time should be considered in this mechanism to ensure the accurecy and efficient. In this paper, considering that the overhead of exact computation varies nonlinearly with fluctuations in data size, random algorithm with smoother complexity change was designed to solve the IM problem in combination with greedy algorithm. Firstly, we proposed a method named two-hop neighbor network influence estimator to evaluate the influence of all nodes in the two-hop neighbor network. Then, we developed a novel greedy algorithm, the random walk probability cost-effective with lazy-forward (RWP-CELF) algorithm by modifying cost-effective with lazy-forward (CELF) with random algorithm, which uses 25–50 orders of magnitude less time than the state-of-the-art algorithms. We compared the influence spread effect of RWP-CELF on real datasets with a theoretically proven algorithm that is guaranteed to be approximately optimal. Experiments show that the spread effect of RWP-CELF is comparable to this algorithm, and the running time is much lower than this algorithm.

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References

  1. Hu X, Chu THS, Leung VCM, Ngai CH, Philippe K, Chan HCB (2015) A survey on mobile social networks: applications, platforms, system architectures, and future research directions. IEEE Commun Surv Tutor 17(3):1557–1581

    Article  Google Scholar 

  2. 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, pp 1029–1038

  3. Budak C, Agrawal D, Abbadi AE (2011) Limiting the spread of misinformation in social networks. In: Proceedings of the 20th international conference on World wide web, pp 665–674

  4. Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining, pp 57–66

  5. Zhang P, Bao Z, Niu Y, Zhang Y, Mo S, Geng F, Peng Z (2019) Proactive rumor control in online networks. World Wide Web 22(4):1799–1818

    Article  Google Scholar 

  6. Li Y, Wang Y, Tan KL (2018) Influence maximization on social graphs: a survey. IEEE Trans Knowl Data Eng 30(10):1852–1872

    Article  Google Scholar 

  7. Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9th international conference on knowledge discovery and data mining, pp 137–146

  8. Banerjee S, Jenamani M, Pratihar DK (2020) A survey on influence maximization in a social network. Knowl Inf Syst 62(9):3417–3455

    Article  Google Scholar 

  9. Banerjee S, Jenamani M, Pratihar DK (2022) An approximate marginal spread computation approach for the budgeted influence maximization with delay. Computing 104:657–680

    Article  MathSciNet  Google Scholar 

  10. Raghavan S, Zhang R R (2015) Weighted target set selection on social networks, Technical report, Working paper, University of Maryland

  11. Azaouzi M, Mnasria W, Romdhane LB (2021) New trends in influence maximization models. Comput Sci Rev 40:100393

    Article  Google Scholar 

  12. Zhu T, Wang B, Wu B, Zhu C (2014) Maximizing the spread of influence ranking in social networks. Inform ci 278:535–544

    MathSciNet  Google Scholar 

  13. Srivastava A, Chelmis C, Prasanna VK (2014) Influence in social networks: a unified model? In: Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining, pp 451–454

  14. Mohamadi-Baghmolaei R, Mozafari N, Hamzeh A (2015) Trust based latency aware influence maximization in social networks. Eng Appl Artif Intell 41:195–206

    Article  Google Scholar 

  15. Wang F, Wang G, Xie D (2016) Maximizing the spread of positive influence under LT-MLA model. In: Asia-Pacific services computing conference, pp 450–463

  16. 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, pp 420–429

  17. Goyal A, Lu W, Lakshmanan LVS (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

  18. 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, pp 199–207

  19. Kundu S, Pal SK (2015) Deprecation based greedy strategy for set selection in large scale social networks. Inf Sci 316:107–122

    Article  Google Scholar 

  20. Shang J, Zhou S, Li X, Liu L, Wu H (2017) CoFIM: a community-based framework for influence maximization on large-scale networks. Knowl-Based Syst 117:88–100

    Article  Google Scholar 

  21. Lu F, Zhang W, Shao L, Jiang X, Xu P, Jin H (2016) Scalable influence maximization under independent cascade model. J Netw Comput Appl 86:15–23

    Article  Google Scholar 

  22. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

    Article  Google Scholar 

  23. Newman MJ (2005) A measure of betweenness centrality based on random walks. Soc Netw 27(1):39–54

    Article  Google Scholar 

  24. Gao M, Xu L, Lin L, Huang Y, Zhang X (2020) Influence maximization based on activity degree in mobile social networks. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.5677

    Article  Google Scholar 

  25. Zhang X, Xu L, Gao M (2020) An efficient influence maximization algorithm based on social relationship priority in mobile social networks. In: International Symposium on Security and Privacy in Social Networks and Big Data. Springer, pp 164–177

  26. Jiang Q, Song G, Cong G, Wang Y, Si W, Xie K (2011) Simulated annealing based influence maximization in social networks. In: Proceeding of the 25th AAAI conference on artificial intelligence, pp 127–132

  27. Cui L, Hu H, Yu S, Yan Q, Ming Z, Wen Z, Lu N (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 

  28. 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(C):600–614

    Article  Google Scholar 

  29. Tang J, Zhang R, Wang P, Zhao Z, Fan L, Liu X (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 

  30. Zhang X, Zhu J, Wang Q, Zhao H (2013) Identifying influential nodes in complex networks with community structure. Knowl-Based Syst 42:74–84

    Article  Google Scholar 

  31. Shang J, Zhou S, Li X, Liu L, Wu H (2017) CoFIM: a community-based framework for influence maximization on large-scale networks. Knowl-Based Syst 117:88–100

    Article  Google Scholar 

  32. Shang J, Wu H, Zhou S, Zhong J, Feng Y, Qiang B (2018) IMPC: influence maximization based on multi-neighbor potential in community networks. Physica A 512:1085–1103

    Article  Google Scholar 

  33. Kazemzadeh F, Safaei AA, Mirzarezaee M, Afsharian S, Kosarirad H (2023) Determination of influential nodes based on the communities structure to maximize influence in social network. Neurocomputing 534:18–28

    Article  Google Scholar 

  34. Kim J, Kim SK, Yu H (2013) Scalable and parallelizable processing of influence maximization for large-scale social networks. In: Proceedings of the IEEE 29th international conference on data engineering, pp 266–277

  35. Liu B, Cong G, Zeng Y, Xu D, Chee YM (2014) Influence spreading path and its application to the time constrained social influence maximization problem and beyond. IEEE Trans Knowl Data Eng 26(8):1904–1917

    Article  Google Scholar 

  36. Christakis NA, Fowler JH (2009) Connected: the surprising power of our social networks and how they shape our lives. Little, Brown, Boston

    Google Scholar 

  37. Pei S, Muchnik L, Andrade JS Jr, Zheng Z, Makse HA (2014) Searching for superspreaders of information in real-world social media. Sci Rep 4:5547

    Article  Google Scholar 

  38. Fu X, Wang C, Wang Z, Ming Z (2013) Threshold random walks for community structure detection in complex networks. J Softw 8(2):286–295

    Article  Google Scholar 

  39. Okuda M, Satoh S, Sato Y, Kidawara Y (2021) Community detection using restrained random-walk similarity. IEEE Trans Pattern Anal Mach Intell 43(1):89–103

    Google Scholar 

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

    Article  Google Scholar 

  41. 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

    Article  MathSciNet  Google Scholar 

  42. Richardson M (2003) Trust management for the semantic web. In: Proceeding of international semantic web conference, pp 351–368

  43. Nguyen HT, Thai MT, Dinh TN (2016) Stop-and-stare: optimal sampling algorithms for viral marketing in billion-scale networks. In: Proceedings of the 2016 international conference on management of data, pp 695–710

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Acknowledgements

The authors would like to thank the National Natural Science Foundation of China No. U1905211, Science and technology projects in Fujian Province NOs. (2022G02003, 2021L3032), Fujian Provincial Department of Education Middle and Young People’s Program No. JAT220814, Enterprise industry-university-research project DH-1565.

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Authors and Affiliations

  1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, China

    Zhenyu Xu, Mingzhi Chen & Li Xu

  2. Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou, Fujian, China

    Zhenyu Xu, Mingzhi Chen & Li Xu

  3. School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, China

    Xinxin Zhang

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  1. Zhenyu Xu
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Correspondence to Li Xu.

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Xu, Z., Zhang, X., Chen, M. et al. Influence maximization in mobile social networks based on RWP-CELF. Computing (2024). https://doi.org/10.1007/s00607-024-01276-z

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