Inference and Validation of Networks

We develop a statistical methodology to validate the result
of network inference algorithms, based on principles of statistical testing
and machine learning. The comparison of results with reference networks,
by means of similarity measures and null models, allows us to measure
the significance of results, as well as their predictive power. The use
of Generalised Linear Models allows us to explain the results in terms
of available ground truth which we expect to be partially relevant. We
present these methods for the case of inferring a network of News Outlets
based on their preference of stories to cover. We compare three simple
network inference methods and show how our technique can be used to
choose between them. All the methods presented here can be directly
applied to other domains where network inference is used.