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A graph is an abstraction of a network and the efficient treatment and analysis of graphs is core to many applications including neural networks, transportation and telecommunication networks, electrical grids, Google’s PageRank and social networks.
In this project, we’ll examine a variety of application areas in order to prioritise which algorithms to implement. One such area is an organ exchange network where patients have willing but incompatible donors. This network is able to give information about compatible transplants along cycles to save lives.
Our project would seek to:
Another application we might explore is deep learning. Recent advances in deep learning require the processing, optimisation and analysis of large directed graphs that are highly symmetric. In order to help design new neural nets, graph parameters give insights about what kind of neural nets work well (the “depth” of networks has led to the well-known term “deep learning”). This project would seek to provide implementations to compute a variety of parameters.
We aim to incorporate a selection of our algorithms into the open-source Python-based software system called SageMath.
All degrees are welcome however, students specialising in the below areas are encouraged to apply.
|Desired Research Areas||Desired Skills|