Participation in INI programmes is by invitation only. Anyone wishing to apply to participate in the associated workshop(s) should use the relevant workshop application form.
Scientific Advisory Committee: Jennifer Chayes (Microsoft Research), Rick Durrett (Duke), Alan Frieze (CMU), Remco van der Hofstad (Eindhoven), Valerie Isham (UCL), Vince Poor (Princeton) and Devavrat Shah (MIT)
The core of this 6-month programme is understanding and quantifying mathematical structure in network models. Networks are ubiquitous in modern science and society. In fact, 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. In support of this goal, the programme aims to:
- Identify core problems in the mathematical foundations of networks whose solution will yield generic tools, thus creating a body of coherent and broadly useful results
- Build a dialogue between the mathematical fields that will contribute to this foundation, and build a community of researchers spanning these areas
- Link these core mathematical problems to important applications, including the modelling and analysis of large-scale real-world networks
To support these aims, four interlinked focus areas will run throughout the programme:
- Statistical Asymptotics of Networks
- Statistical Models for Networks
- Large-Scale Algorithms
- Dynamics in Networks
The programme will feature an opening, midterm and closing workshop at the Isaac Newton Institute, as well as a Satellite Meeting and an Open for Business industry day.