Making sense of social networks
Developing new local and global measures for networks derived from social interactions to infer social structure, sentiment and behaviour.
Real-world networks are complicated beasts. In order to capture all of the detailed time evolution and correlations that drive the nuances of these networks, it is necessary to model them extremely accurately. However, their non-linear nature can make their analysis intractable, or very computationally expensive at best. Instead, measures must be sought that encapsulate the important and defining aspects of these networks, which can then be analysed in full.
This project defines new local and global measures of networks which are related to their geometries. The notion of graph temperature enables the prediction of distinct topological properties of real-world networks simultaneously, whilst the hyperbolicity of a network is able to measure how democratic or aristocratic it is. From these properties it is possible to deduce the sentiment and behaviour of social and economic networks over time.
The ability to predict how a social or economic network will behave or react in a given circumstance is a powerful tool for driving social and economic growth. Modelling and analysing these networks using ideas and measures from graph theory makes this possible, and paves the way for a fuller understanding of the interconnected nature of real-world networks.
Analysis of the hyperbolicity of real-world networks distinguishes between those which are aristocratic and those which are democratic.
A new concept, graph temperature, enables the prediction of distinct topological properties of real-world networks simultaneously.