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  Applications of graph theory in data science


   Faculty of Engineering & Digital Technologies

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  Dr Ci Lei, Prof Apostol Vourdas  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The aim of the project is to use and apply the discrete mathematics and data visualisation techniques such as graphs, trees, combinatorics etc, to enhance existing clustering techniques in big graphs with potential applications such as nature language processing. The study will be largely based on research of existing methodologies, review of discrete mathematics and data visualisation techniques with a focus on graph and text mining literature and algorithms.
Computer Science (8) Materials Science (24)

Funding Notes

This is a self-funded PhD project; the applicants will be expected to pay their own fees and living costs and / or seek separate funding from student finance, charities or trusts. A bench fee of £5000 per year also applies in addition to tuition fees.

References

1. Liu, Yike, Safavi, Tara, Dighe, Abhilash and Koutra, Danai, Graph Summarization Methods and Applications: A Survey. ACM Comput. Surv. 51, 34 (2018)

2. Lei, C. and Vourdas, A. Selective correlations in finite quantum systems and the Desargues property. J. Geom. Phys. 128, 118 (2018)
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