Davis, M.C., Ma, Z., Liu, W., Miller, P., Hunter, R., Kee, F. Generating Realistic Labelled, Weighted Random Graphs. Algorithms 2015, 8, 1143-1174. Davis MC, Ma Z, Liu W, Miller P, Hunter R, Kee F. Generating Realistic Labelled, Weighted Random Graphs. Algorithms. 2015, 8(4):1143-1174. Davis, Michael C., Ma, Zhanyu, Liu, Weiru, Miller, Paul, Hunter, Ruth, Kee, Frank. 2015. ‘Generating Realistic Labelled, Weighted Random Graphs.’ Algorithms 8, no. 4: 1143-1174. Novel Meta-heuristic Approaches and Their Applications to Preemptive Operational Planning and Logistics in Disaster Management Pattern Recognition and Intelligent Systems (PRIS) Lab, Beijing University of Posts and Telecommunications (BUPT), 100876 Beijing, China School of Electrical and Electronic Engineering and Computer Science, Queen’s University Belfast, University Road, Belfast BT7 1NN, UK This paper is an extended version of our paper published in New Frontiers in Mining Complex Patterns—Second International Workshop (NFMCP 2013), held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013), Prague, Czech Republic, 27 September 2013. 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. network models, generative algorithms, random graphs, labelled graphs, weighted graphs, bayesian estimation, maximum likelihood estimation, beta distribution, mixture modeling, variational inference which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Davis, Michael C., Ma, Zhanyu, Liu, Weiru, Miller, Paul, Hunter, Ruth, Kee, Frank. 2015. ‘Generating Realistic Labelled, Weighted Random Graphs.’ Source.