Likelihood based framework for evolving graphs

UCL Statistics

This talk is the latest of my talks about FETA the framework for evolving topology analysis. This uses updated notation. The core of the work is a likelihood based model which can assess how likely it is that observations of the evolution of a graph arise from a particular probabilistic model, for example a model such as the Barabassi-Albert preferential attachment model. Analysis is given to data from Facebook and from Enron as well as from artificial models.