Abstract
A key challenge to ensuring robustness of complex systems is to correctly identify component systems, which we simply call nodes, that are more likely to trigger cascading failures. A recent approach takes advantage of the relationship between the cascading failure probability and the non-backtracking centrality of nodes when the Perron-Frobenius (P-F) eigenvalue of the associated non-backtracking matrix is close to one. However, this assumption is not guaranteed to hold in practice. Motivated by this observation, we propose a new approach that does not require the P-F eigenvalue to be close to one, and demonstrate that it offers good accuracy and outperforms the non-backtracking centrality-based approach for both synthetic and real networks.
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Acknowledgment
This work was supported in part by contract 70NANB16H024 from National Institute of Standards and Technology (NIST).
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La, R.J. (2020). Identifying Vulnerable Nodes to Cascading Failures: Optimization-Based Approach. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_64
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DOI: https://doi.org/10.1007/978-3-030-36687-2_64
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