Abstract
We address the problem of efficiently detecting critical links in a large network. Critical links are such links that their deletion exerts substantial effects on the network performance such as the average node reachability. We tackle this problem by proposing a new method which consists of one existing and two new acceleration techniques: redundant-link skipping (RLS), marginal-node pruning (MNP) and burn-out following (BOF). All of them are designed to avoid unnecessary computation and work both in combination and in isolation. We tested the effectiveness of the proposed method using two real-world large networks and two synthetic large networks. In particular, we showed that the new method can compute the performance degradation by link removal without introducing any approximation within a comparable computation time needed by the bottom-k sketch which is a summary of dataset and can efficiently process approximate queries, i.e., reachable nodes, on the original dataset, i.e., the given network.
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References
Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406, 378–382 (2000)
Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2014), pp. 946–957 (2014)
Chen, W., Lakshmanan, L., Castillo, C.: Information and influence propagation in social networks. Synth. Lect. Data Manag. 5(4), 1–177 (2013)
Cohen, E.: Size-estimation framework with applications to transitive closure and reachability. J. Comput. Syst. Sci. 55, 441–453 (1997)
Cohen, E., Delling, D., Pajor, T., Werneck, R.F.: Sketch-based influence maximization and computation: scaling up with guarantees. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 629–638 (2014)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. Theory Comput. 11, 105–147 (2015)
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)
Kimura, M., Saito, K., Ohara, K., Motoda, H.: Speeding-up node influence computation for huge social networks. Int. J. Data Sci. Anal. 1, 1–14 (2016)
Newman, M.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)
Saito, K., Kimura, M., Ohara, K., Motoda, H.: Detecting critical links in complex network to maintain information flow/reachability. In: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence, pp. 419–432 (2016)
Watts, D.: A simple model of global cascades on random networks. Proc. Natl. Acad. Sci. U. S. A. 99, 5766–5771 (2002)
Acknowledgments
This material is based upon work supported by the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD) under award number FA2386-16-1-4032, and JSPS Grant-in-Aid for Scientific Research (C) (No. 17K00314).
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Saito, K., Ohara, K., Kimura, M., Motoda, H. (2017). An Accurate and Efficient Method to Detect Critical Links to Maintain Information Flow in Network. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_12
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DOI: https://doi.org/10.1007/978-3-319-60438-1_12
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