27,436 research outputs found
Entropy Optimization of Scale-Free Networks Robustness to Random Failures
Many networks are characterized by highly heterogeneous distributions of
links, which are called scale-free networks and the degree distributions follow
. We study the robustness of scale-free networks to
random failures from the character of their heterogeneity. Entropy of the
degree distribution can be an average measure of a network's heterogeneity.
Optimization of scale-free network robustness to random failures with average
connectivity constant is equivalent to maximize the entropy of the degree
distribution. By examining the relationship of entropy of the degree
distribution, scaling exponent and the minimal connectivity, we get the optimal
design of scale-free network to random failures. We conclude that entropy of
the degree distribution is an effective measure of network's resilience to
random failures.Comment: 9 pages, 5 figures, accepted by Physica
Analysis of network by generalized mutual entropies
Generalized mutual entropy is defined for networks and applied for analysis
of complex network structures. The method is tested for the case of computer
simulated scale free networks, random networks, and their mixtures. The
possible applications for real network analysis are discussed
Quantifying Link Stability in Ad Hoc Wireless Networks Subject to Ornstein-Uhlenbeck Mobility
The performance of mobile ad hoc networks in general and that of the routing
algorithm, in particular, can be heavily affected by the intrinsic dynamic
nature of the underlying topology. In this paper, we build a new
analytical/numerical framework that characterizes nodes' mobility and the
evolution of links between them. This formulation is based on a stationary
Markov chain representation of link connectivity. The existence of a link
between two nodes depends on their distance, which is governed by the mobility
model. In our analysis, nodes move randomly according to an Ornstein-Uhlenbeck
process using one tuning parameter to obtain different levels of randomness in
the mobility pattern. Finally, we propose an entropy-rate-based metric that
quantifies link uncertainty and evaluates its stability. Numerical results show
that the proposed approach can accurately reflect the random mobility in the
network and fully captures the link dynamics. It may thus be considered a
valuable performance metric for the evaluation of the link stability and
connectivity in these networks.Comment: 6 pages, 4 figures, Submitted to IEEE International Conference on
Communications 201
Information transfer of an Ising model on a brain network
We implement the Ising model on a structural connectivity matrix describing
the brain at a coarse scale. Tuning the model temperature to its critical
value, i.e. at the susceptibility peak, we find a maximal amount of total
information transfer between the spin variables. At this point the amount of
information that can be redistributed by some nodes reaches a limit and the net
dynamics exhibits signature of the law of diminishing marginal returns, a
fundamental principle connected to saturated levels of production. Our results
extend the recent analysis of dynamical oscillators models on the connectome
structure, taking into account lagged and directional influences, focusing only
on the nodes that are more prone to became bottlenecks of information. The
ratio between the outgoing and the incoming information at each node is related
to the number of incoming links
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