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Network immunization under limited budget using graph spectra

Author

Listed:
  • R. Zahedi

    (Department of Science and Engineering, Sharif University of Technology, Amir Kabir Square, Kish Island, Iran)

  • M. Khansari

    (#x2020;Faculty of New Sciences and Technologies, University of Tehran, Amir Abad, North Kargar Street, Tehran 14395-1374, Iran)

Abstract

In this paper, we propose a new algorithm that minimizes the worst expected growth of an epidemic by reducing the size of the largest connected component (LCC) of the underlying contact network. The proposed algorithm is applicable to any level of available resources and, despite the greedy approaches of most immunization strategies, selects nodes simultaneously. In each iteration, the proposed method partitions the LCC into two groups. These are the best candidates for communities in that component, and the available resources are sufficient to separate them. Using Laplacian spectral partitioning, the proposed method performs community detection inference with a time complexity that rivals that of the best previous methods. Experiments show that our method outperforms targeted immunization approaches in both real and synthetic networks.

Suggested Citation

  • R. Zahedi & M. Khansari, 2016. "Network immunization under limited budget using graph spectra," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(08), pages 1-10, August.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:08:n:s0129183116500960
    DOI: 10.1142/S0129183116500960
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