Abstract
In this paper, we focus on an emergency situation in the real-world such as disaster evacuation and propose an algorithm that can efficiently identify critical links in a spatial network that substantially degrade network performance if they fail to function. For that purpose, we quantify the network performance by node reachability from/to one of target facilities within the prespecified time limitation, which corresponds to the number of people who can safely evacuate in a disaster. Using a real-world road network and geographical information of actual facilities, we demonstrated that the proposed method is much more efficient than the method based on the betweenness centrality that is one of the representative centrality measures and that the critical links detected by our method cannot be identified by using a straightforward extension of the betweenness centrality.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Akram, V.K., Dagdeviren, O.: Breadth-first search-based single-phase algorithms for bridge detection in wireless sensor networks. Sensors 13(7), 8786–8813 (2013)
Barabási, A.L.: Network Science. Cambridge University Press, Cambridge (2016)
Brandes, U., Erlebach, T. (eds.): Network Analysis: Methodological Foundations. LNCS, vol. 3418. Springer, Heidelberg (2005). https://doi.org/10.1007/b106453
Burckhart, K., Martin, O.J.: An interpretation of the recent evolution of the city of barcelona through the traffic maps. J. Geogr. Inf. Syst. 4(4), 298–311 (2012)
Crucitti, P., Latora, V., Porta, S.: Centrality measures in spatial networks of urban streets. Phys. Rev. E 73(3), 036125 (2006)
Fang, Y.P., Pedroni, N., Zio, E.: Comparing network-centric and power flow models for the optimal allocation of link capacities in a cascade-resilient power transmission network. IEEE Syst. J. 99, 1–12 (2014)
Grady, D., Thiemann, C., Brockmann, D.: Robust classification of salient links in complex networks. Nature Commun. 3(864), 1–10 (2012)
Kimura, M., Saito, K., Motoda, H.: Blocking links to minimize contamination spread in a social network. ACM Trans. Knowl. Discov. Data 3, 9:1–9:23 (2009)
Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O.: Centrality indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, pp. 16–61. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31955-9_3
Montis, D.A., Barthelemy, M., Chessa, A., Vespignani, A.: The structure of interurban traffic: a weighted network analysis. Environ. Plan. 34(5), 905–924 (2007)
Ohara, K., Saito, K., Kimura, M., Motoda, H.: Accelerating computation of distance based centrality measures for spatial networks. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 376–391. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46307-0_24
Ohara, K., Saito, K., Kimura, M., Motoda, H.: Maximizing network performance based on group centrality by creating most effective k-links. In: Proceedings of the 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017), pp. 561–570 (2017)
Oliveira, E.L., Portugal, L.S., Junior, W.P.: Determining critical links in a road network: vulnerability and congestion indicators. Procedia Soc. Behav. Sci. 162, 158–167 (2014)
Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)
Park, K., Yilmaz, A.: A social network analysis approach to analyze road networks. In: Proceedings of the ASPRS Annual Conference 2010 (2010)
Piraveenan, M., Prokopenko, M., Hossein, L.: Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks. PLoS ONE 8(1), 1–14 (2013)
Saito, K., Ohara, K., Kimura, M., Motoda, H.: Accurate and efficient detection of critical links in network to minimize information loss. J. Intell. Inf. Syst. 51(2), 235–255 (2018)
Saito, K., Ohara, K., Kimura, M., Motoda, H.: Efficient detection of critical links to maintain performance of network with uncertain connectivity. In: Geng, X., Kang, B.-H. (eds.) PRICAI 2018. LNCS (LNAI), vol. 11012, pp. 282–295. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97304-3_22
Sariyüce, A.E., Kaya, K., Saule, E., Çatalyürek, U.V.: Graph manipulations for fast centrality computation. ACM Trans. Knowl. Discov. Data 11(3), 26:1–26:25 (2017)
Shen, Y., Nguyen, N.P., Xuan, Y., Thai, M.T.: On the discovery of critical links and nodes for assessing network vulnerability. IEEE/ACM Trans. Networking 21(3), 963–973 (2013)
Tarjan, R.E.: A note on finding the bridges of a graph. Inf. Process. Lett. 2(6), 160–161 (1974)
Wang, P., Hunter, T., Bayen, A.M., Schechtner, K., Gonzalez, M.C.: Understanding road usage patterns in urban areas. Sci. Rep. 2, 1001:1–1001:6 (2012)
Acknowledgments
This material is based upon work supported by JSPS Grant-in-Aid for Scientific Research (C) (No. 17K00314).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Saito, K., Ohara, K., Kimura, M., Motoda, H. (2019). Efficient Identification of Critical Links Based on Reachability Under the Presence of Time Constraint. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-29911-8_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29910-1
Online ISBN: 978-3-030-29911-8
eBook Packages: Computer ScienceComputer Science (R0)