167 research outputs found
A Convex Framework for Optimal Investment on Disease Awareness in Social Networks
We consider the problem of controlling the propagation of an epidemic
outbreak in an arbitrary network of contacts by investing on disease awareness
throughout the network. We model the effect of agent awareness on the dynamics
of an epidemic using the SAIS epidemic model, an extension of the SIS epidemic
model that includes a state of "awareness". This model allows to derive a
condition to control the spread of an epidemic outbreak in terms of the
eigenvalues of a matrix that depends on the network structure and the
parameters of the model. We study the problem of finding the cost-optimal
investment on disease awareness throughout the network when the cost function
presents some realistic properties. We propose a convex framework to find
cost-optimal allocation of resources. We validate our results with numerical
simulations in a real online social network.Comment: IEEE GlobalSIP Symposium on Network Theor
ELASTICITY: Topological Characterization of Robustness in Complex Networks
Just as a herd of animals relies on its robust social structure to survive in
the wild, similarly robustness is a crucial characteristic for the survival of
a complex network under attack. The capacity to measure robustness in complex
networks defines the resolve of a network to maintain functionality in the
advent of classical component failures and at the onset of cryptic malicious
attacks. To date, robustness metrics are deficient and unfortunately the
following dilemmas exist: accurate models necessitate complex analysis while
conversely, simple models lack applicability to our definition of robustness.
In this paper, we define robustness and present a novel metric, elasticity- a
bridge between accuracy and complexity-a link in the chain of network
robustness. Additionally, we explore the performance of elasticity on Internet
topologies and online social networks, and articulate results
Effect of Coupling on the Epidemic Threshold in Interconnected Complex Networks: A Spectral Analysis
In epidemic modeling, the term infection strength indicates the ratio of
infection rate and cure rate. If the infection strength is higher than a
certain threshold -- which we define as the epidemic threshold - then the
epidemic spreads through the population and persists in the long run. For a
single generic graph representing the contact network of the population under
consideration, the epidemic threshold turns out to be equal to the inverse of
the spectral radius of the contact graph. However, in a real world scenario it
is not possible to isolate a population completely: there is always some
interconnection with another network, which partially overlaps with the contact
network. Results for epidemic threshold in interconnected networks are limited
to homogeneous mixing populations and degree distribution arguments. In this
paper, we adopt a spectral approach. We show how the epidemic threshold in a
given network changes as a result of being coupled with another network with
fixed infection strength. In our model, the contact network and the
interconnections are generic. Using bifurcation theory and algebraic graph
theory, we rigorously derive the epidemic threshold in interconnected networks.
These results have implications for the broad field of epidemic modeling and
control. Our analytical results are supported by numerical simulations.Comment: 7 page
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