1,614 research outputs found
Estimating Infection Sources in Networks Using Partial Timestamps
We study the problem of identifying infection sources in a network based on
the network topology, and a subset of infection timestamps. In the case of a
single infection source in a tree network, we derive the maximum likelihood
estimator of the source and the unknown diffusion parameters. We then introduce
a new heuristic involving an optimization over a parametrized family of Gromov
matrices to develop a single source estimation algorithm for general graphs.
Compared with the breadth-first search tree heuristic commonly adopted in the
literature, simulations demonstrate that our approach achieves better
estimation accuracy than several other benchmark algorithms, even though these
require more information like the diffusion parameters. We next develop a
multiple sources estimation algorithm for general graphs, which first
partitions the graph into source candidate clusters, and then applies our
single source estimation algorithm to each cluster. We show that if the graph
is a tree, then each source candidate cluster contains at least one source.
Simulations using synthetic and real networks, and experiments using real-world
data suggest that our proposed algorithms are able to estimate the true
infection source(s) to within a small number of hops with a small portion of
the infection timestamps being observed.Comment: 15 pages, 15 figures, accepted by IEEE Transactions on Information
Forensics and Securit
Network segregation in a model of misinformation and fact checking
Misinformation under the form of rumor, hoaxes, and conspiracy theories
spreads on social media at alarming rates. One hypothesis is that, since social
media are shaped by homophily, belief in misinformation may be more likely to
thrive on those social circles that are segregated from the rest of the
network. One possible antidote is fact checking which, in some cases, is known
to stop rumors from spreading further. However, fact checking may also backfire
and reinforce the belief in a hoax. Here we take into account the combination
of network segregation, finite memory and attention, and fact-checking efforts.
We consider a compartmental model of two interacting epidemic processes over a
network that is segregated between gullible and skeptic users. Extensive
simulation and mean-field analysis show that a more segregated network
facilitates the spread of a hoax only at low forgetting rates, but has no
effect when agents forget at faster rates. This finding may inform the
development of mitigation techniques and overall inform on the risks of
uncontrolled misinformation online
Backward and Forward Inference in Interacting Independent-Cascade Processes: A Scalable and Convergent Message-Passing Approach
We study the problems of estimating the past and future evolutions of two
diffusion processes that spread concurrently on a network. Specifically, given
a known network and a (possibly noisy) snapshot
of its state taken at (a possibly unknown) time , we wish to
determine the posterior distributions of the initial state of the network and
the infection times of its nodes. These distributions are useful in finding
source nodes of epidemics and rumors -- -- , and
estimating the spread of a fixed set of source nodes -- .
To model the interaction between the two processes, we study an extension of
the independent-cascade (IC) model where, when a node gets infected with either
process, its susceptibility to the other one changes. First, we derive the
exact joint probability of the initial state of the network and the
observation-snapshot . Then, using the machinery of
factor-graphs, factor-graph transformations, and the generalized
distributive-law, we derive a Belief-Propagation (BP) based algorithm that is
scalable to large networks and can converge on graphs of arbitrary topology (at
a likely expense in approximation accuracy)
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