Network analysis characterizes networked structures in terms of nodes and the ties, edges, or links that connect them. Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, demography, communication studies, economics, geography, history, information science, organizational studies, political science, social psychology, development studies, sociolinguistics, and computer science.
The Mitchell Centre for Social Network Analysis is a cross-disciplinary research group located in the School of Social Sciences. The centre’s mission is to be a world leading centre in the development and application of social network analysis techniques. They aim to establish an international centre of excellence for social network analysis within the UK, to establish a central resource and reference point for social network researchers and users both within the UK and internationally, to cultivate existing interest and further stimulate interest in social network analysis in the UK and beyond and to make important contributions to the social network analysis literature.
Martin is the head of the Mitchell Centre and a Chair in Network Network Analysis at the University. His main research interest is in mathematical or analytical sociology and he is principally a methodologist focusing in the area of social networks. In particular he's interested in new non-statistical and descriptive methods such as centrality, positional analysis techniques and cohesive subgroups for networks and in the analysis of non-standard network data such as two-mode networks and networks containing negative ties.
Some of his current projects include: Covert social networks - the project focuses on finding some concrete evidence for claims about covert social networks. One key claim is that these networks are organized to facilitate their aims but this is often overridden by their need to keep their connections hidden. This has consequences for the resulting structures which he aims to test empirically. Networks with negative ties - Techniques for analyzing networks that contain both positive and negative ties. This is work he is undertaking with Steve Borgatti from the University of Kentucky. Networks of people with severe mental illness - The aim is to see if we can identify structural properties that will help us identify when an early intervention would be beneficial.
Johan is a Lecturer in Social Statistics at the University of Manchester. He has contributed to the development of a number of statistical models and inference procedures for social networks, in particular exponential random graph models (ERGM) and stochastic actor-oriented models (SAOM). His methodological contributions are often developed in collaboration over substantive research projects with applied researchers and he is active in disseminating best practices through frequent workshops. His current research concentrates on extending current statistical methodology for modelling social interaction to social networks of multiple types of nodes using data collated and collected from different sources. He frequently gives training workshops on statistical methods for social networks to both novices and advanced users of social network analysis and he co-edited a recently published introductory book on ERGM with Dean Lusher and Gary Robins at the Universities of Melbourne and Swinburne.
Thomas works primarily on mathematical epidemiology, an area of interdisciplinary applied mathematics that involves a large number of quantitative techniques applied to the study of patterns of disease at the population level. Epidemiology is the science of counting ill people - a simple description that hides major mathematical complexity. A tremendous number of different factors combine to determine the risk that any of us might become ill, however if we could disentangle these from each other then it might be possible to target the most important factors systematically. His work is mainly on the mathematical, statistical and computational methods that are needed to understand these phenomena. Thomas currently works on the EPSRC-funded project 'Operationalising Modern Mathematical Epidemiology'.
Other Researchers across the University:
Robert is a Dame Kathleen Ollerenshaw Fellow at the School of Mathematics. His research interests include distributional approximations and Stein's method, probability distributions, networks, special functions. In his Network analysis work he looks at Poisson and compound Poisson approximation of the distribution of subgraph counts in stochastic block models and random graphs with multiple edges, along with network comparison and development of new measures to assess network similarity.