Abstract
Efficiently identifying critical links that substantially degrade network performance if they fail to function is challenging for a large complex network. In this paper, we tackle this problem under a more realistic situation where each link is probabilistically disconnected as if a road is blocked in a natural disaster than assuming that any road is never blocked in a disaster. To solve this problem, we utilize the bridge detection technique in graph theory and efficiently identify critical links in case the node reachability is taken as the performance measure, which corresponds to the number of people who can reach at least one evacuation facility in a disaster. Using two real-world road networks, we empirically show that the proposed method is much more efficient than the other methods that are based on traditional centrality measures and the links our method detected are substantially more critical than those by the others.
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Notes
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We implemented our programs in C, and conducted our experiments on a computer system with a single thread (Xeon X5690 3.47 GHz CPUs) within a 192GB main memory capacity.
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Acknowledgments
This material is based upon work supported by JSPS Grant-in-Aid for Scientific Research (C) (No. 17K00314).
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Saito, K., Ohara, K., Kimura, M., Motoda, H. (2018). Critical Link Identification Based on Bridge Detection for Network with Uncertain Connectivity. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_9
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