5 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
Epidemic Spreading in Interdependent Networks
We introduce a dynamic process of epidemic spreading into interdependent networks because, in reality, interdependent networks are facing the threat of epidemic spreading, which still lacks research. With our model, we reveal that (i) interdependent networks are more fragile than an isolated single network when facing the threat of an epidemic; (ii) an epidemic is not massively blocked by cascading failure, even if cascading failure spreads much faster; (iii) interdependent networks are more fragile, with a larger value of average degree compared with epidemic spreading. Moreover, we propose an iterative method for estimating the fraction of removed or failed nodes in a system of interdependent networks using percolation theory. This research can improve the comprehension of interdependent networks from the point of view of epidemic spreading