2 research outputs found
Bayesian Inference of Diffusion Networks with Unknown Infection Times
The analysis of diffusion processes in real-world propagation scenarios often
involves estimating variables that are not directly observed. These hidden
variables include parental relationships, the strengths of connections between
nodes, and the moments of time that infection occurs. In this paper, we propose
a framework in which all three sets of parameters are assumed to be hidden and
we develop a Bayesian approach to infer them. After justifying the model
assumptions, we evaluate the performance efficiency of our proposed approach
through numerical simulations on synthetic datasets and real-world diffusion
processes
Bayesian inference of network structure from information cascades
Contagion processes are strongly linked to the network structures on which
they propagate, and learning these structures is essential for understanding
and intervention on complex network processes such as epidemics and
(mis)information propagation. However, using contagion data to infer network
structure is a challenging inverse problem. In particular, it is imperative to
have appropriate measures of uncertainty in network structure estimates,
however these are largely ignored in most machine-learning approaches. We
present a probabilistic framework that uses samples from the distribution of
networks that are compatible with the dynamics observed to produce network and
uncertainty estimates. We demonstrate the method using the well known
independent cascade model to sample from the distribution of networks P(G)
conditioned on the observation of a set of infections C. We evaluate the
accuracy of the method by using the marginal probabilities of each edge in the
distribution, and show the bene ts of quantifying uncertainty to improve
estimates and understanding, particularly with small amounts of data