Package for "pruning" weighted complex networks based on the Marginal Likelihood Filter.

When dealing with weighted complex networks of dyadic relationships between nodes, we frequently encounter overly-dense "hairball" networks where the large number of edges may obfuscate the most important structures within the graph.

People often "prune" the graph by simply setting a threshold on the edge weights and removing all edges below the threshold. However, this simplistic approach systematically disfavours low-degree nodes and the structures they represent.Therefore, we need to measure the significance of an edge, with respect to the degrees of its incident vertices.

Here, we do this using a version of the configuration model as our null model that defines what we expect an edge’s weight to be given its end-nodes’ degrees. The null model predicts a probability distribution for the value of the edge weight. By comparing the observed weight of an edge with this predicted distribution, we can then compute a p-value which tells us how surprising the observed value is given the null model. This p-value gives us a measure of the statistical significance of the edge, and we can filter the edges according to this significance rather than the raw weight itself.

• More: http://www.naviddianati.com/research
• Link to paper: http://journals.aps.org/pre/abstract/10.1103/PhysRevE.93.012304 (preprint: http://arxiv.org/abs/1503.04085)
• HTML version of paper: http://www.naviddianati.com/papers/graphpruning/html/manuscript.html