4 research outputs found
Statistical inference framework for source detection of contagion processes on arbitrary network structures
In this paper we introduce a statistical inference framework for estimating
the contagion source from a partially observed contagion spreading process on
an arbitrary network structure. The framework is based on a maximum likelihood
estimation of a partial epidemic realization and involves large scale
simulation of contagion spreading processes from the set of potential source
locations. We present a number of different likelihood estimators that are used
to determine the conditional probabilities associated to observing partial
epidemic realization with particular source location candidates. This
statistical inference framework is also applicable for arbitrary compartment
contagion spreading processes on networks. We compare estimation accuracy of
these approaches in a number of computational experiments performed with the
SIR (susceptible-infected-recovered), SI (susceptible-infected) and ISS
(ignorant-spreading-stifler) contagion spreading models on synthetic and
real-world complex networks
A Robust Information Source Estimator with Sparse Observations
In this paper, we consider the problem of locating the information source
with sparse observations. We assume that a piece of information spreads in a
network following a heterogeneous susceptible-infected-recovered (SIR) model
and that a small subset of infected nodes are reported, from which we need to
find the source of the information. We adopt the sample path based estimator
developed in [1], and prove that on infinite trees, the sample path based
estimator is a Jordan infection center with respect to the set of observed
infected nodes. In other words, the sample path based estimator minimizes the
maximum distance to observed infected nodes. We further prove that the distance
between the estimator and the actual source is upper bounded by a constant
independent of the number of infected nodes with a high probability on infinite
trees. Our simulations on tree networks and real world networks show that the
sample path based estimator is closer to the actual source than several other
algorithms