171 research outputs found
Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks
In complex networks, the failure of one or very few nodes may cause cascading
failures. When this dynamical process stops in steady state, the size of the
giant component formed by remaining un-failed nodes can be used to measure the
severity of cascading failures, which is critically important for estimating
the robustness of networks. In this paper, we provide a cascade of overload
failure model with local load sharing mechanism, and then explore the threshold
of node capacity when the large-scale cascading failures happen and un-failed
nodes in steady state cannot connect to each other to form a large connected
sub-network. We get the theoretical derivation of this threshold in
degree-degree uncorrelated networks, and validate the effectiveness of this
method in simulation. This threshold provide us a guidance to improve the
network robustness under the premise of limited capacity resource when creating
a network and assigning load. Therefore, this threshold is useful and important
to analyze the robustness of networks.Comment: 11 pages, 4 figure
Dynamic Behavior of Interacting between Epidemics and Cascades on Heterogeneous Networks
Epidemic spreading and cascading failure are two important dynamical
processes over complex networks. They have been investigated separately for a
long history. But in the real world, these two dynamics sometimes may interact
with each other. In this paper, we explore a model combined with SIR epidemic
spreading model and local loads sharing cascading failure model. There exists a
critical value of tolerance parameter that whether the epidemic with high
infection probability can spread out and infect a fraction of the network in
this model. When the tolerance parameter is smaller than the critical value,
cascading failure cuts off abundant of paths and blocks the spreading of
epidemic locally. While the tolerance parameter is larger than the critical
value, epidemic spreads out and infects a fraction of the network. A method for
estimating the critical value is proposed. In simulation, we verify the
effectiveness of this method in Barab\'asi-Albert (BA) networks
Layer-switching cost and optimality in information spreading on multiplex networks
We study a model of information spreading on multiplex networks, in which
agents interact through multiple interaction channels (layers), say online vs.\
offline communication layers, subject to layer-switching cost for transmissions
across different interaction layers. The model is characterized by the
layer-wise path-dependent transmissibility over a contact, that is dynamically
determined dependently on both incoming and outgoing transmission layers. We
formulate an analytical framework to deal with such path-dependent
transmissibility and demonstrate the nontrivial interplay between the
multiplexity and spreading dynamics, including optimality. It is shown that the
epidemic threshold and prevalence respond to the layer-switching cost
non-monotonically and that the optimal conditions can change in abrupt
non-analytic ways, depending also on the densities of network layers and the
type of seed infections. Our results elucidate the essential role of
multiplexity that its explicit consideration should be crucial for realistic
modeling and prediction of spreading phenomena on multiplex social networks in
an era of ever-diversifying social interaction layers.Comment: 15 pages, 7 figure
Critical phenomena in complex networks
The combination of the compactness of networks, featuring small diameters,
and their complex architectures results in a variety of critical effects
dramatically different from those in cooperative systems on lattices. In the
last few years, researchers have made important steps toward understanding the
qualitatively new critical phenomena in complex networks. We review the
results, concepts, and methods of this rapidly developing field. Here we mostly
consider two closely related classes of these critical phenomena, namely
structural phase transitions in the network architectures and transitions in
cooperative models on networks as substrates. We also discuss systems where a
network and interacting agents on it influence each other. We overview a wide
range of critical phenomena in equilibrium and growing networks including the
birth of the giant connected component, percolation, k-core percolation,
phenomena near epidemic thresholds, condensation transitions, critical
phenomena in spin models placed on networks, synchronization, and
self-organized criticality effects in interacting systems on networks. We also
discuss strong finite size effects in these systems and highlight open problems
and perspectives.Comment: Review article, 79 pages, 43 figures, 1 table, 508 references,
extende
Towards real-world complexity: an introduction to multiplex networks
Many real-world complex systems are best modeled by multiplex networks of
interacting network layers. The multiplex network study is one of the newest
and hottest themes in the statistical physics of complex networks. Pioneering
studies have proven that the multiplexity has broad impact on the system's
structure and function. In this Colloquium paper, we present an organized
review of the growing body of current literature on multiplex networks by
categorizing existing studies broadly according to the type of layer coupling
in the problem. Major recent advances in the field are surveyed and some
outstanding open challenges and future perspectives will be proposed.Comment: 20 pages, 10 figure
Exact solutions for social and biological contagion models on mixed directed and undirected, degree-correlated random networks
We derive analytic expressions for the possibility, probability, and expected
size of global spreading events starting from a single infected seed for a
broad collection of contagion processes acting on random networks with both
directed and undirected edges and arbitrary degree-degree correlations. Our
work extends previous theoretical developments for the undirected case, and we
provide numerical support for our findings by investigating an example class of
networks for which we are able to obtain closed-form expressions.Comment: 10 pages, 3 figure
Epidemic spreading and bond percolation on multilayer networks
The Susceptible-Infected-Recovered (SIR) model is studied in multilayer
networks with arbitrary number of links across the layers. By following the
mapping to bond percolation we give the analytical expression for the epidemic
threshold and the fraction of the infected individuals in arbitrary number of
layers. These results provide an exact prediction of the epidemic threshold for
infinite locally tree-like multilayer networks, and an lower bound of the
epidemic threshold for more general multilayer networks. The case of a
multilayer network formed by two interconnected networks is specifically
studied as a function of the degree distribution within and across the layers.
We show that the epidemic threshold strongly depends on the degree correlations
of the multilayer structure. Finally we relate our results to the results
obtained in the annealed approximation for the Susceptible-Infected-Susceptible
(SIS) model.Comment: 8 pages, 2 figure
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