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Sequential seeding for spreading in complex networks: Influence of the network topology

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  • Liu, Qipeng
  • Hong, Tao

Abstract

In this paper we investigate the problem of sequential seeding for spreading in complex networks. We focus on the influence of network topology on the performance of seeding strategies. The classic independent cascade model (ICM) is adopted to represent the spreading process. We examine the centrality measures—degree, K-shell, and H-index in several real networks and confirm that degree is a good indicator for spreading efficiency. Scale-free networks with tunable parameters such as power-law exponent, density, and assortativity coefficient are constructed as the testbed of the study. By simulations, we find that the advantage of sequential seeding strategy is large in a degree-heterogeneous network with relatively small average degree and large assortativity coefficient.

Suggested Citation

  • Liu, Qipeng & Hong, Tao, 2018. "Sequential seeding for spreading in complex networks: Influence of the network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 10-17.
    • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:10-17
    DOI: 10.1016/j.physa.2018.05.057
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    References listed on IDEAS

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    1. Xu, Shuang & Wang, Pei, 2017. "Identifying important nodes by adaptive LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 654-664.
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    5. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
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    Cited by:

    1. Ni, Chengzhang & Yang, Jun & Kong, Demei, 2020. "Sequential seeding strategy for social influence diffusion with improved entropy-based centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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