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SMG: Fast scalable greedy algorithm for influence maximization in social networks

Author

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  • Heidari, Mehdi
  • Asadpour, Masoud
  • Faili, Hesham

Abstract

Influence maximization is the problem of finding k most influential nodes in a social network. Many works have been done in two different categories, greedy approaches and heuristic approaches. The greedy approaches have better influence spread, but lower scalability on large networks. The heuristic approaches are scalable and fast but not for all type of networks. Improving the scalability of greedy approach is still an open and hot issue. In this work we present a fast greedy algorithm called State Machine Greedy that improves the existing algorithms by reducing calculations in two parts: (1) counting the traversing nodes in estimate propagation procedure, (2) Monte-Carlo graph construction in simulation of diffusion. The results show that our method makes a huge improvement in the speed over the existing greedy approaches.

Suggested Citation

  • Heidari, Mehdi & Asadpour, Masoud & Faili, Hesham, 2015. "SMG: Fast scalable greedy algorithm for influence maximization in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 124-133.
    • Handle: RePEc:eee:phsmap:v:420:y:2015:i:c:p:124-133
    DOI: 10.1016/j.physa.2014.10.088
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    References listed on IDEAS

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    1. CORNUEJOLS, Gérard & FISHER, Marshall L. & NEMHAUSER, George L., 1977. "Location of bank accounts to optimize float: An analytic study of exact and approximate algorithms," LIDAM Reprints CORE 292, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Gerard Cornuejols & Marshall L. Fisher & George L. Nemhauser, 1977. "Exceptional Paper--Location of Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms," Management Science, INFORMS, vol. 23(8), pages 789-810, April.
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    Cited by:

    1. Xiaodong Liu & Xiangke Liao & Shanshan Li & Si Zheng & Bin Lin & Jingying Zhang & Lisong Shao & Chenlin Huang & Liquan Xiao, 2017. "On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks," Complexity, Hindawi, vol. 2017, pages 1-14, September.
    2. Charikhi, Mourad, 2024. "Association of the PageRank algorithm with similarity-based methods for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    3. Li, Shudong & Zhao, Dawei & Wu, Xiaobo & Tian, Zhihong & Li, Aiping & Wang, Zhen, 2020. "Functional immunization of networks based on message passing," Applied Mathematics and Computation, Elsevier, vol. 366(C).
    4. Wang, Qiyao & Jin, Yuehui & Lin, Zhen & Cheng, Shiduan & Yang, Tan, 2016. "Influence maximization in social networks under an independent cascade-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 20-34.
    5. Tang, Jianxin & Zhang, Ruisheng & Yao, Yabing & Yang, Fan & Zhao, Zhili & Hu, Rongjing & Yuan, Yongna, 2019. "Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 477-496.

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