Name
Abstract | ||
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We propose a new model for the study of resilience of coevolving multiplex scale-free networks. Our network model, called preferential interdependent networks, is a novel continuum over scale-free networks parameterized by their correlation ρ, 0 ≤ ρ ≤ 1. Our failure and recovery model ties the propensity of a node, both to fail and to assist in recovery, to its importance. We show, analytically, that our network model can achieve any γ, 2 ≤ γ ≤ 3 for the exponent of the power law of the degree distribution; this is superior to existing multiplex models and allows us better fidelity in representing real-world networks. Our failure and recovery model is also a departure from the much studied cascading error model based on the giant component; it allows for surviving important nodes to send assistance to the damaged nodes to enable their recovery. This better reflects the reality of recovery in man-made networks such as social networks and infrastructure networks.
Our main finding, based on simulations, is that resilient preferential interdependent networks are those in which the layers are neither completely correlated (ρ = 1) nor completely uncorrelated (ρ = 0) but instead semi-correlated (ρ ≈ 0.1 -- 0.3). This finding is consistent with the real-world experience where complex man-made networks typically bounce back quickly from stress. In an attempt to explain our intriguing empirical discovery we present an argument for why semi-correlated multiplex networks can be the most resilient. Our argument can be seen as an explanation of plausibility or as an incomplete mathematical proof subject to certain technical conjectures that we make explicit.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3382225.3382274 | ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining
Barcelona
Spain
August, 2018 |
Keywords | Field | DocType |
multiplex scale-free networks,network model,multiplex models,real-world networks,social networks,infrastructure networks,resilient preferential interdependent networks,complex man-made networks,semicorrelated multiplex networks,interdependent multiplex networks coevolution,preferential interdependent networks | Psychological resilience,Data mining,Interdependent networks,Parameterized complexity,Fidelity,Computer science,Giant component,Theoretical computer science,Mathematical proof,Degree distribution,Network model | Conference |
ISSN | ISBN | Citations |
2473-9928 | 978-1-5386-6051-5 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Auroop R. Ganguly | 1 | 286 | 29.53 |
| Tanbay Mehta | 2 | 0 | 0.34 |
| Ravi Sundaram | 3 | 762 | 72.13 |
| Devesh Tiwari | 4 | 55 | 14.20 |