Background
Networks are ubiquitous in modern science and society - many complex systems can be described in terms of networks capturing the intricate web of connections among the units they are made up of. Whenever we observe entities and relationships between them, we have network data. The behaviour of almost all networks, natural or engineered, physical or information-based, involves a strong component of randomness and is typically not fully or directly observed. Considerable open challenges remain in proving properties both of generative mechanisms for such networks, as well as of methods for inference. This motivates the development of theoretical foundations for statistical network analysis.
This workshop, part of the Isaac Newton Institute Research Programme on Theoretical Foundations for Statistical Network Analysis, seeks to bring together experts and stakeholders to discuss network problems faced in industry and the public sector and to facilitate interactions to identify future activities.
Register http://www.turing-gateway.cam.ac.uk/register?event=ofbw27
Aims and Objectives
The overall aim of this workshop is to encourage interaction between participants from different disciplines, to facilitate cross-disciplinary discussion.
Policy decisions and much scientific research hinge on accurate and comprehensive evidence and data. Those making these decisions need access to statistical modelling approaches, through new approaches that can accommodate randomness and dependencies observed in network analysis.
Analysing large data sets has become more and more important, but tackling the complexity of data sets has often been challenging, as has how to visualise and analyse networks meaningfully.
The workshop will aim to explore some of these challenges and highlight recent developments in the field and applications within industry, which may catalyse joint projects.