Journal
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
Volume 3, Issue 4, Pages 348-357Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCNS.2015.2476197
Keywords
Community structure; network modularity; robustness analysis
Funding
- National Research Foundation of Korea (NRF) - Korea Government
- Ministry of Science, ICT & Future Planning [2014R1A2A1A10052404, 2013M3A9A7046303]
- EPSRC [EP/G036195/1]
- KAIST Future Systems Healthcare Project from the Ministry of Science, ICT & Future Planning
- GIST Systems Biology Infrastructure Establishment Grant
- KUSTAR-KAIST Institute, Korea, under the RD program
- Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea [HI13C2162]
- EPSRC [EP/G036195/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/G036195/1] Funding Source: researchfish
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Modules are commonly observed functional units in large-scale networks and the dynamics of networks are closely related to the organization of such modules. Modularity analysis has been widely used to investigate the organizing principle of complex networks. The information about network topology needed for such modularity analysis is, however, not complete in many real-world networks. We noted that the network structure is often reconstructed based on partial observation and, therefore, it is re-organized as more information is collected. Hence, it is critical to evaluate the robustness of network modules with respect to uncertainties. For this purpose, we have developed a robustness bounds algorithm that provides an estimation of the unknown minimal perturbation, which breaks down the original modularity. The proposed algorithm is computationally efficient and provides valuable information about the robustness of modularity for large-scale network analysis.
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