Literature DB >> 31511422

What network motifs tell us about resilience and reliability of complex networks.

Asim K Dey1, Yulia R Gel1, H Vincent Poor2.   

Abstract

Network motifs are often called the building blocks of networks. Analysis of motifs has been found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. This phenomenon of the impact of local structure has been recently documented in network fragility analysis and classification. At the same time, many studies of networks still tend to focus on global topological measures, often failing to unveil hidden mechanisms behind vulnerability of real networks and their dynamic response to malfunctions. In this paper, a study of motif-based analysis of network resilience and reliability under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of networks. These methods are demonstrated on electricity transmission networks of 4 European countries, and the results are compared with commonly used resilience and reliability measures.

Keywords:  complex networks; data depth; multivariate reliability; network motifs; network resilience

Year:  2019        PMID: 31511422      PMCID: PMC6765276          DOI: 10.1073/pnas.1819529116

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


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Journal:  Phys Rev E       Date:  2016-09-09       Impact factor: 2.529

9.  How dead ends undermine power grid stability.

Authors:  Peter J Menck; Jobst Heitzig; Jürgen Kurths; Hans Joachim Schellnhuber
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10.  Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks.

Authors:  Thomas E Gorochowski; Claire S Grierson; Mario di Bernardo
Journal:  Sci Adv       Date:  2018-03-28       Impact factor: 14.136

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