Network Science is a very active and rapidly evolving research field with high societal impact, which stands at the crossroads of graph theory, complexity and data analysis. The goal of network science is to develop tools to analyse Big Data of interacting complex networks and to propose numerical and analytical frameworks to predict their behaviour.
Since in these decades we are witnessing an exponential growth of data concerning communications networks and global infrastructures, the financial system, on-line social networks and biological networks, Network Science stands as a new discipline to cope with some of the most challenging endeavours we face today, in an ever increasingly more connected society.
Its impact and applications outside academia pervades technological sectors, finance, marketing and IT, public health and network biology, to cite a few.
This specialist masters programme aims at providing graduate students and professionals with a rigorous training in the underlying mathematical concepts, the analysis and modelling of complex networks and networked systems, complemented with training in computing, numerical simulations and massive data analysis. It is aimed towards students with a mathematical background who wish to enter a career involving analysis and optimisation of diverse kinds of networks, networked dynamics and models.
Why study with us?
- Training in the most recent advances of Network Science
- Numerical simulations and analysis of Big Data
- Interactions with leading experts of Network Science
- Prepares for employment in Data Science, consulting finance, software and for research in Network Science
Our links with industry
We offer all students the opportunity to take the prestigious Microsoft Office Specialist qualification
Citi sponsor Maths careers events on campus
Dr Ginestra Bianconi is Reader in Applied Mathematics and Director of the MSc in Network Science at the School of Mathematical Sciences at Queen Mary University of London, London, UK. Her research activity on network science includes network theory and its applications in social and technological networks, bioinformatics and neuroscience. She has formulated the Bianconi-Barabasi model that explains the winner-take-all phenomena in complex networks, and she has worked in network entropy and dynamical processes on networks. In the last years she has been particularly active on multilayer networks, network geometry, percolation and network control. Her work has appeared in journals such as Science, PNAS, PRX and Physical Review Letters.
Harvie Kwan Chiu (MSc Network Science graduate, 2016)
"This programme allows you to focus on the underlying mathematical concepts, analysis, and modelling of complex networks but also to gain important transferable skills in computing and data analysis; I highly recommend it."