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MassExodus: modeling evolving networks in harsh environments

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Abstract

Consider networks in harsh environments, where nodes may be lost due to failure, attack, or infection—how is the topology affected by such events? Can we mimic and measure the effect? We propose a new generative model of network evolution in dynamic and harsh environments. Our model can reproduce the range of topologies observed across known robust and fragile biological networks, as well as several additional transport, communication, and social networks. We also develop a new optimization measure to evaluate robustness based on preserving high connectivity following random or adversarial bursty node loss. Using this measure, we evaluate the robustness of several real-world networks and propose a new distributed algorithm to construct secure networks operating within malicious environments.

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Acknowledgments

This material is based upon work supported by the National Institutes of Health award no. F32-MH099784 to S.N; by the National Science Foundation under Grant No. IIS-1217559, by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, and by a Google Focused Research Award to C.F; and by grants from the McDonnell Foundation programme on Studying Complex Systems and from the US National Science Foundation award nos. DBI-0965316 and DBI-1356505 to Z.B.-J. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other funding parties. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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Authors and Affiliations

  1. Center for Integrative Biology, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA

    Saket Navlakha

  2. Machine Learning Department, Computer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Christos Faloutsos

  3. Machine Learning Department, Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Ziv Bar-Joseph

Authors
  1. Saket Navlakha
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  2. Christos Faloutsos
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Correspondence to Saket Navlakha.

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Responsible editor: Joao Gama, Indre Zliobaite and Alipio Jorge.

S. Navlakha’s study began during a post-doc at Carnegie Mellon University.

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Navlakha, S., Faloutsos, C. & Bar-Joseph, Z. MassExodus: modeling evolving networks in harsh environments. Data Min Knowl Disc 29, 1211–1232 (2015). https://doi.org/10.1007/s10618-014-0399-1

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