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International Conference on Complex Networks and their Applications

COMPLEX NETWORKS 2018: Complex Networks and Their Applications VII pp 866–878Cite as

Identifying Vulnerable Nodes to Cascading Failures: Centrality to the Rescue

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 812))

Abstract

We study the problem of identifying nodes that are more likely to trigger cascading failures in complex systems, which we call vulnerable nodes. We show that there is a close relation between the likelihood of a node setting off cascading failures (which we call the cascading failure probability) and its non-backtracking centrality; when every failed node is equally likely to cause the failure of each neighbor, the cascading failure probability and non-backtracking centrality of a node are proportional to each other. Based on this observation, we propose a new approach to finding vulnerable nodes and study its performance using numerical studies.

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Notes

  1. 1.

    These dependence relations are not necessarily the physical links in a network. For example, in a power system, an overload failure in one part of power grid can cause a failure in another part that is not geographically close or without direct physical connection to the former.

  2. 2.

    It is shown that the degree distribution of many real networks can be approximated using a power law [1].

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Acknowledgement

This work was supported in part by contract 70NANB16H024 from National Institute of Standards and Technology (NIST).

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Authors and Affiliations

  1. University of Maryland, College Park, MD, 20742, USA

    Richard J. La

Authors
  1. Richard J. La
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Corresponding author

Correspondence to Richard J. La .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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La, R.J. (2019). Identifying Vulnerable Nodes to Cascading Failures: Centrality to the Rescue. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_69

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