Human mobility: Can you quantify group behaviour through temporal graph models?
Introduction
In the past years, human mobility has received a surge of interest from the research community, mainly in the context of COVID-19. Posed as a dynamic temporal graph modelling problem, the main body of literature on human mobility bases itself on link-based approaches. Most of these models are not able to accurately reproduce the typical dynamics of contacts between individuals as they fail to learn a governing graph generative process. Our research proposes to model dynamic human contacts from first principles and develop a framework for quantifying the class of processes which generate temporal graphs.
The Random Walker Induced temporal Graph (RWIG) model is conceptually intuitive: a collection of random walkers traverse a Markov graph in discrete time steps, with co-location of any pair of walkers in a Markov state resulting in a link in their contact graph. We have developed a comprehensive theory around the probability distribution of the RWIG model, and we wish to further understand how the process behaves in the steady-state.
Project
The student is expected to:
- Develop a good understanding of the RWIG model
- Perform large-scale simulations of RWIG using various input parameters
- Conduct both theoretical and empirical analysis of the probability distribution of RWIG graphs when the random walkers are in the steady-state.
Requirements
You are in the final stage of your degree in computer science, mathematics, electrical engineering or a similar degree. You are expected to have strong programming skills and experience in Markov theory and network science. You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.
To apply
Contact David Almasan,