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Accurate and efficient detection of critical links in network to minimize information loss

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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 three 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 proposed method can estimate the performance degradation by link removal without introducing any approximation within a computation time comparable to that 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. Further, we confirmed that the measures easily composed by the well known existing centralities, e.g. in/out-degree, betweenness, PageRank, authority/hub, are not able to detect critical links. Links detected by these measures do not reduce the average reachability at all, i.e., not critical at all.

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  1. https://snap.stanford.edu/

  2. https://snap.stanford.edu/data/cit-HepPh.html

  3. https://snap.stanford.edu/data/p2p-Gnutella30.html

References

  • Albert, R., Jeong, H., Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.

    Article  Google Scholar 

  • Boldi, P., & Vigna, S. (2013). In-core computation of geometric centralities with hyperball: a hunderd billion nodes and beyond. In Proceedings of the 2013 IEEE 13th international conference on data mining workshops (ICDMW’13) (pp. 621–628).

  • Bonchi, F., Castillo, C., Ienco, D. (2013). Meme ranking to maximize posts virality in microblogging platforms. Journal of Intelligent Information Systems, 40, 211–239.

    Article  Google Scholar 

  • Borgs, C., Brautbar, M., Chayes, J., Lucier, B. (2014). Maximizing social influence in nearly optimal time. In Proceedings of the 25th annual ACM-SIAM symposium on discrete algorithms (SODA’14) (pp. 946–957).

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30, 107–117.

    Article  Google Scholar 

  • Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J. (2000). Graph structure in the web. In Proceedings of the 9th international world wide web conference (pp. 309–320).

  • Callaway, D.S., Newman, M.E.J., Strogatz, S.H., Watts, D.J. (2000). Network robustness and fragility: percolation on random graphs. Physical Reveiw Letters, 85, 5468–5471.

    Article  Google Scholar 

  • Chakrabarti, S., Dom, B., Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A., Gibson, D., Kleinberg, J. (1999). Mining the web’s link structure. IEEE Computer, 32, 60–67.

    Article  Google Scholar 

  • Chen, W., Lakshmanan, L., Castillo, C. (2013). Information and influence propagation in social networks. Synthesis Lectures on Data Management, 5(4), 1–177.

    Article  Google Scholar 

  • Chierichetti, F., Epasto, A., Kumar, R., Lattanzi, S., Mirrokni, V. (2015). Efficient algorithms for public-private social networks. In Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining (KDD’15) (pp. 139–148).

  • Cohen, E. (1997). Size-estimation framework with applications to transitive closure and reachability. Journal of Computer and System Sciences, 55, 441–453.

    Article  MathSciNet  MATH  Google Scholar 

  • Cohen, E., Delling, D., Pajor, T., Werneck, R.F. (2014). Sketch-based influence maximization and computation: scaling up with guarantees. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management (pp. 629–638).

  • Freeman, L. (1979). Centrality in social networks: conceptual clarification. Social Networks, 1, 215– 239.

    Article  Google Scholar 

  • Kempe, D., Kleinberg, J., Tardos, E. (2003). Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’03) (pp. 137–146).

  • Kempe, D., Kleinberg, J., Tardos, E. (2015). Maximizing the spread of influence through a social network. Theory of Computation, 11, 105–147.

    Article  MathSciNet  MATH  Google Scholar 

  • Kimura, M., Saito, K., Motoda, H. (2009). Blocking links to minimize contamination spread in a social network. ACM Transactions on Knowledge Discovery from Data, 3, 9:1–9:23.

    Article  Google Scholar 

  • Kimura, M., Saito, K., Nakano, R. (2007). Extracting influential nodes for information diffusion on a social network. In Proceedings of the 22nd AAAI conference on artificial intelligence (AAAI-07) (pp. 1371–1376).

  • Kimura, M., Saito, K., Ohara, K., Motoda, H. (2014). Efficient analysis of node influence based on sir model over huge complex networks. In Proceedings of the 2014 international conference on data science and advanced analytics (DSAA’14) (pp. 216–222).

  • Kimura, M., Saito, K., Ohara, K., Motoda, H. (2016). Speeding-up node influence computation for huge social networks. International Journal of Data Science and Analytics, 1, 1–14.

    Article  Google Scholar 

  • Newman, M. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.

    Article  MathSciNet  MATH  Google Scholar 

  • Newman, M.E.J., Forrest, S., Balthrop, J. (2002). Email networks and the spread of computer viruses. Physical Review E, 66(035), 101.

    Google Scholar 

  • Ohara, K., Saito, K., Kimura, M., Motoda, H. (2014). Resampling-based framework for estimating node centrality of large social network. In Proceedings of the 17th international conference on discovery science (DS’14) (pp. 228–239). LNAI 8777.

  • Saito, K., Kimura, M., Ohara, K., Motoda, H. (2016). 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).

  • 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 Proceedings of the 23th international symposium on methodologies for intelligent systems (pp. 116–126).

  • Song, G., Zhou, X., Wang, Y., Xie, K. (2015). Influence maximization on large-scale mobile social network: a divide-and-conquer method. IEEE Transactions on Parallel and Distributed Systems, 26, 1379–1392.

    Article  Google Scholar 

  • Thapa, M., Espejo-Uribe, J., Pournaras, E. (2017). Measuring network reliability and repairability against cascading failures. Journal of Intelligent Information Systems. https://doi.org/10.1007/s10844-017-0477-0. Online First.

  • Watts, D. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences of the United States of America, 99, 5766–5771.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhou, C., Zhang, P., Zang, W., Guo, L. (2015). On the upper bounds of spread for greedy algorithms in social network influence maximization. IEEE Transactions on Knowledge and Data Engineering, 27, 2770–2783.

    Article  Google Scholar 

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Correspondence to Kazumi Saito.

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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. et al. Accurate and efficient detection of critical links in network to minimize information loss. J Intell Inf Syst 51, 235–255 (2018). https://doi.org/10.1007/s10844-018-0523-6

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  • DOI: https://doi.org/10.1007/s10844-018-0523-6

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