87 citations · 87 across the 2 of their papers we have counts for
11 papers
Approximate sampling and estimation of partition functions using neural networks
George T. Cantwell
We consider the closely related problems of sampling from a distribution known up to a normalizing constant, and estimating said normalizing constant. We show how variational autoe…
The friendship paradox in real and model networks
George T. Cantwell, Alec Kirkley, M. E. J. Newman
The friendship paradox is the observation that the degrees of the neighbors of a node in any network will, on average, be greater than the degree of the node itself. In common parl…
Belief propagation for networks with loops
Alec Kirkley, George T. Cantwell, M. E. J. Newman
Belief propagation is a widely used message passing method for the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models,…
Bayesian inference of network structure from unreliable data
Jean-Gabriel Young, George T. Cantwell, M. E. J. Newman
Most empirical studies of complex networks do not return direct, error-free measurements of network structure. Instead, they typically rely on indirect measurements that are often…
Inference for growing trees
George T. Cantwell, Guillaume St-Onge, Jean-Gabriel Young
One can often make inferences about a growing network from its current state alone. For example, it is generally possible to determine how a network changed over time or pick among…
Improved mutual information measure for classification and community detection
M. E. J. Newman, George T. Cantwell, Jean-Gabriel Young
The information theoretic quantity known as mutual information finds wide use in classification and community detection analyses to compare two classifications of the same set of o…