142 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
Modeling the propagation and defense study of internet malicious information
Dr. Wen\u27s research includes modelling the propagation dynamics of malicious information, exposing the most influential people and source identification of epidemics in social networks. His research is beneficial to both academia and industry in the field of Internet social networks
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