Extreme events are emergent phenomena in multi-particle transport processes on complex networks. In practice, such events could range from power blackouts to call drops in cellular networks to traffic congestion on roads. All the earlier studies of extreme events on complex networks had focused only on the nodal events. If random walks are used to model the transport process on a network, it is known that degree of the nodes determines the extreme event properties. In contrast, in this work, it is shown that extreme events on the edges display a distinct set of properties from that of the nodes. It is analytically shown that the probability for the occurrence of extreme events on an edge is independent of the degree of the nodes linked by the edge and is dependent only on the total number of edges on the network and the number of walkers on it. Further, it is also demonstrated that non-trivial correlations can exist between the extreme events on the nodes and the edges. These results are in agreement with the numerical simulations on synthetic and real-life networks.
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April 2020
Research Article|
April 09 2020
Extreme events in stochastic transport on networks
Special Collection:
Rare Events in Complex Systems: Understanding and Prediction
Aanjaneya Kumar;
Aanjaneya Kumar
Department of Physics, Indian Institute of Science Education and Research
, Dr. Homi Bhabha Road, Pune 411008, India
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Suman Kulkarni;
Suman Kulkarni
Department of Physics, Indian Institute of Science Education and Research
, Dr. Homi Bhabha Road, Pune 411008, India
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M. S. Santhanam
M. S. Santhanam
a)
Department of Physics, Indian Institute of Science Education and Research
, Dr. Homi Bhabha Road, Pune 411008, India
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a)
Author to whom correspondence should be addressed: santh@iiserpune.ac.in
Note: This article is part of the Focus Issue, Rare Events in Complex Systems: Understanding and Prediciton.
Chaos 30, 043111 (2020)
Article history
Received:
November 18 2019
Accepted:
March 24 2020
Citation
Aanjaneya Kumar, Suman Kulkarni, M. S. Santhanam; Extreme events in stochastic transport on networks. Chaos 1 April 2020; 30 (4): 043111. https://doi.org/10.1063/1.5139018
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