161 research outputs found
Link-based formalism for time evolution of adaptive networks
Network topology and nodal dynamics are two fundamental stones of adaptive
networks. Detailed and accurate knowledge of these two ingredients is crucial
for understanding the evolution and mechanism of adaptive networks. In this
paper, by adopting the framework of the adaptive SIS model proposed by Gross et
al. [Phys. Rev. Lett. 96, 208701 (2006)] and carefully utilizing the
information of degree correlation of the network, we propose a link-based
formalism for describing the system dynamics with high accuracy and subtle
details. Several specific degree correlation measures are introduced to reveal
the coevolution of network topology and system dynamics.Comment: 12 pages, 8 figure
Network infection source identification under the SIRI model
We study the problem of identifying a single infection source in a network
under the susceptible-infected-recovered-infected (SIRI) model. We describe the
infection model via a state-space model, and utilizing a state propagation
approach, we derive an algorithm known as the heterogeneous infection spreading
source (HISS) estimator, to infer the infection source. The HISS estimator uses
the observations of node states at a particular time, where the elapsed time
from the start of the infection is unknown. It is able to incorporate side
information (if any) of the observed states of a subset of nodes at different
times, and of the prior probability of each infected or recovered node to be
the infection source. Simulation results suggest that the HISS estimator
outperforms the dynamic message pass- ing and Jordan center estimators over a
wide range of infection and reinfection rates.Comment: 5 pages, 3 figures; to present in ICASSP 201
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