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Identification of influential spreaders in bipartite networks:A singular value decomposition approach

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  • Xu, Shuang
  • Wang, Pei
  • Zhang, Chunxia

Abstract

A bipartite network is a graph that contains two disjoint sets of nodes, such that every edge connects the two node sets. The significance of identifying influential nodes in bipartite networks is highlighted from both theoretical and practical perspectives. By considering the unique feature of bipartite networks, namely, links between the same node set are forbidden, we propose two new algorithms, called SVD-rank and SVDA-rank respectively. In the two algorithms, singular value decomposition (SVD) is performed on the original bipartite network and augmented network (two ground nodes are added). Susceptible–Infected–Recovered (SIR) model is employed to evaluate the performance of the two algorithms. Simulations on seven real-world networks show that the proposed algorithms can well identify influential spreaders in bipartite networks, and the two algorithms are robust to network perturbations. The proposed algorithms may have potential applications in the control of bipartite networks.

Suggested Citation

  • Xu, Shuang & Wang, Pei & Zhang, Chunxia, 2019. "Identification of influential spreaders in bipartite networks:A singular value decomposition approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 297-306.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:297-306
    DOI: 10.1016/j.physa.2018.09.005
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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.
    2. Zhang, Peng & Wang, Jinliang & Li, Xiaojia & Li, Menghui & Di, Zengru & Fan, Ying, 2008. "Clustering coefficient and community structure of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6869-6875.
    3. Namtirtha, Amrita & Dutta, Animesh & Dutta, Biswanath, 2018. "Identifying influential spreaders in complex networks based on kshell hybrid method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 310-324.
    4. Linyuan Lü & Tao Zhou & Qian-Ming Zhang & H. Eugene Stanley, 2016. "The H-index of a network node and its relation to degree and coreness," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    5. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    6. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
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    Cited by:

    1. Tripathi, Richa & Reza, Amit, 2020. "A subset selection based approach to structural reducibility of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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