Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

Truth at 24 frames a second? A working dialogue between a film-maker and a historical consultant about the making of The Enigma of Frank Ryan

We employ a practice-based methodology based on a ‘live’ film project to explore the different ways that film-makers and historians narrate the past. Through a case-study of the production and exhibition of a drama-documentary feature-film, The Enigma of Frank Ryan, on which both authors (film-maker Bell and historian McGarry) worked respectively as director and historical consultant, we explore a range of critical issues arising from our collaboration. Through a dialogue between a director and a historian, a model of good practice between historians and film-makers emerges.