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• W2894763075 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." @default.
• W2894763075 created "2018-10-12" @default.
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• W2894763075 date "2018-01-01" @default.
• W2894763075 modified "2023-09-23" @default.
• W2894763075 title "Critical Link Identification Based on Bridge Detection for Network with Uncertain Connectivity" @default.
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• W2894763075 doi "https://doi.org/10.1007/978-3-030-01851-1_9" @default.
• W2894763075 hasPublicationYear "2018" @default.
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