Abstract
We address the problem of efficiently detecting critical links in a large network in order to maintain network performance, e.g., in case of disaster evacuation, for which a probabilistic link disconnection model plays an essential role. Here, critical links are such links that their disconnection exerts substantial effects on the network performance such as the average node reachability. We tackle this problem by proposing a new method consisting of two new acceleration techniques: reachability condition skipping (RCS) and distance constraints skipping (DCS). We tested the effectiveness of the proposed method by using three real-world spatial networks. In particular, we show that the proposed method achieves the efficiency gain of around \(10^4\) compared with a naive method in which every single link is blindly tested as a critical link candidate.
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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). Efficient Detection of Critical Links to Maintain Performance of Network with Uncertain Connectivity. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_22
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DOI: https://doi.org/10.1007/978-3-319-97304-3_22
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