3 research outputs found
Dynamic Network Model from Partial Observations
Can evolving networks be inferred and modeled without directly observing
their nodes and edges? In many applications, the edges of a dynamic network
might not be observed, but one can observe the dynamics of stochastic cascading
processes (e.g., information diffusion, virus propagation) occurring over the
unobserved network. While there have been efforts to infer networks based on
such data, providing a generative probabilistic model that is able to identify
the underlying time-varying network remains an open question. Here we consider
the problem of inferring generative dynamic network models based on network
cascade diffusion data. We propose a novel framework for providing a
non-parametric dynamic network model--based on a mixture of coupled
hierarchical Dirichlet processes-- based on data capturing cascade node
infection times. Our approach allows us to infer the evolving community
structure in networks and to obtain an explicit predictive distribution over
the edges of the underlying network--including those that were not involved in
transmission of any cascade, or are likely to appear in the future. We show the
effectiveness of our approach using extensive experiments on synthetic as well
as real-world networks
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
A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs
As the availability and importance of temporal interaction data--such as
email communication--increases, it becomes increasingly important to understand
the underlying structure that underpins these interactions. Often these
interactions form a multigraph, where we might have multiple interactions
between two entities. Such multigraphs tend to be sparse yet structured, and
their distribution often evolves over time. Existing statistical models with
interpretable parameters can capture some, but not all, of these properties. We
propose a dynamic nonparametric model for interaction multigraphs that combines
the sparsity of edge-exchangeable multigraphs with dynamic clustering patterns
that tend to reinforce recent behavioral patterns. We show that our method
yields improved held-out likelihood over stationary variants, and impressive
predictive performance against a range of state-of-the-art dynamic graph
models