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On the influence maximization problem and the percolation phase transition

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  • Kolumbus, Yoav
  • Solomon, Sorin

Abstract

We analyze the problem of network influence maximization in the uniform independent cascade model: Given a network with N nodes and a probability p for a node to contaminate a neighbor, find a set of k initially contaminated nodes that maximizes the expected number of eventually contaminated nodes. This problem is of interest theoretically and for many applications in social networks. Unfortunately, it is a NP-hard problem. Using Percolation Theory, we show that in practice the problem is hard only in a vanishing neighborhood of a critical value p=pc. For p>pc there exists a “Giant Cluster” of order N, that is easily found in finite time. For p

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  • Kolumbus, Yoav & Solomon, Sorin, 2021. "On the influence maximization problem and the percolation phase transition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    • Handle: RePEc:eee:phsmap:v:573:y:2021:i:c:s0378437121002004
    DOI: 10.1016/j.physa.2021.125928
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    Cited by:

    1. Han, Jihui & Zhang, Ge & Dong, Gaogao & Zhao, Longfeng & Shi, Yuefeng & Zou, Yijiang, 2024. "Exact analysis of generalized degree-based percolation without memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    2. Kazemzadeh, Farzaneh & Safaei, Ali Asghar & Mirzarezaee, Mitra, 2022. "Influence maximization in social networks using effective community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).

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