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 communication 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 pervade 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 your MSc Network Science at Queen Mary University of London?
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
This programme is run jointly by the School of Mathematical Sciences and the School of Electronic Engineering and Computer Science and is offered full time (one year) and part-time (two years). Full time students will take four modules per semester, followed by a 10,000 word dissertation. Additionally, our students will benefit from special lectures with our industrial stakeholders and from research open days with our research group.
Semester 1 - Compulsory modules
- Graphs and Networks
- Research Methods in Mathematical Sciences
- Topics in Scientific Computing
Semester 1 - Elective modules
- Data Mining
- Dynamical Systems
- Machine Learning
Semester 2 - Compulsory modules
- Processes on Networks
- Digital Media and Social Networks
Semester 2 - Elective modules
- Complex Systems
- Computational Statistics with R
- Database Systems
- Trading and Risk Systems Development
- Machine Learning with Python
Part-time Part-time study options often mean that the number of modules taken is reduced per semester, with the full modules required to complete the programme spread over two academic years. Teaching is generally done during the day and part-time students should contact the course convenor to get an idea of when these teaching hours are likely to take place. Timetables are likely to be finalised in September but you may be able to gain an expectation of what will be required.
Graphs and Networks Networks characterize the underlying structure of a large variety of complex systems, from the Internet to social networks and the brain. This course is designed to teach students the mathematical language needed to describe complex networks, their basic properties and dynamics. The broad aim is to provide students with the key skills required fundamental research in complex networks, and necessary for application of network theory to specific network problems arising in academic or industrial environments. The students will acquire experience in solving problems related to complex networks and will learn the necessary language to formulate models of network-embedded systems.
Topics in Scientific Computing This module covers the use of computers & programming language (mainly Matlab) for solving applied mathematical problems in general, and problems in network science in particular. Its aim is to provide students with computational tools to solve problems they are likely to encounter in networks (search algorithms, generate network ensembles, ...) and in more generic applied mathematics problems (numerical solution of ordinary differential equations, random number generation) as well as to provide them with a sound understanding of a programming language used in applied sciences.
Research Methods in Mathematical Sciences This module is designed to provide students with the skills and expertise to access, read and understand research literature in a wide range of mathematics and its applications. In addition, students will gain the necessary background for delivering efficient and professional oral presentations, poster presentations and scientific writing. Finally, the course is aimed to constitute a guide as well as a first training in research oriented tools and careers.
Processes on Networks Networks characterize the underlying structure of a large variety of complex systems, from the Internet to social networks and the brain. Models of complex systems therefore address dynamical processes taking place on top of these networks. For example, we search and navigate the Internet, opinions or infections spread on social networks, or neurons in the brain synchronize their dynamics. In this module students will learn the fundamental results on various dynamical process defined on complex networks, including random walks, percolation, epidemic spreading and synchronization, as well as applications and implications for real systems: from the PageRank algorithm as a fundamental method to search the Internet, to the effect of percolation on scale-free networks as a method to understand the robustness of many biological and technological networks to random failures or targeted attacks.
Digital Media and Social Networks The fast rise in adoption of Online Social Networks (OSN) and digital media has evolved the way users interact on the Internet, in a manner that most personal communication is now taking place via such tools. The adoption of services such as Facebook, Twitter and YouTube affect the traffic patterns on the Internet as well. Recently, there have been a large number of studies on measurement and analysis of user connectivity, data sharing and traffic patterns on the Internet focusing on OSNs. This course covers different aspects of the concepts of OSN, recommender systems, user behaviour, advertising and privacy. The focus will be on the concepts around measurement, analysis, usability and privacy aspects of OSNs.
Dissertation All students will have to deliver a final dissertation. We have a large pool of possible projects that range all areas of Network Science, linked to active research topics within the Complex Systems & Networks (CSN) group at the School of Mathematical Sciences. All projects will therefore have a large component of original work and cutting-edge research, and will be supervised by members of the CSN group. Additionally, a number of projects will be co-supervised by one of our sponsor company, Neo4j, for those students which are interested in developing a project with direct impact in network technologies.
Machine Learning This course covers methods for machine learning from signals and data, including statistical pattern recognition methods, neural networks, and clustering. The aim of the course is to give you an understanding of machine learning methods, including pattern recognition, clustering and neural networks, and to allow you to apply such methods in a range of areas. By the end of the course you will be able to: Recall a range of machine learning techniques and algorithms, including neural networks and statistical methods; Use concepts from probability theory in machine learning; Derive and analyse properties of machine learning methods; Discuss the relative merits of different machine learning techniques and approaches and apply machine learning methods to the analysis of signals and data.
Data mining This is an introductory module that answers the question "how to extract meaningful and relevant information from a big dataset?", providing skills and tools to succeed in the era of big data.
Dynamical Systems A dynamical system is any system which evolves over time according to some pre-determined rule. The goal of dynamical systems theory is to understand this evolution. This module develops the theory of dynamical systems systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations and chaos. Much emphasis is placed on applications.
Complex Systems This module introduces the students to the exciting field of complexity and complex systems, systems composed by several units that interact nonlinearly and whose macroscopic behaviour cannot be understood
by looking at individual elements. The concepts of emergence and self-organisation will be thoroughly studied, and concrete topics include coupled and time-delayed dynamical systems (bifurcations, stability, chaos, Lyapunov exponents), basic stochastic processes, time series (measures of dimensions, entropies and complexity), fractals, multifractals, and particle models. Focus will be both on mathematical modelling aspects and on computational and numerical methods.
Computational statistics with R This module introduces modern methods of statistical inference for small samples, which use computational methods of analysis, rather than asymptotic theory. Some of these methods such as permutation tests and bootstrapping, are now used regularly in modern business, finance and science. The topics covered include nonparametric tests, cross-validation, model selection, and boostrapping among others.
Trading and Risk Systems Development This course covers the fundamentals of development of financial applications based on a three tiered architecture. It will combine the use of Excel as a front end, VBA as middleware, and C++ as a compute engine to illustrate current practices in the financial industry. The course will emphasize code development best practices and object oriented development.
Machine Learning with Python This course aims at providing students with Machine Learning skills based on the Python programming language as it is currently used in industry. Some of the presented methods are regression and classification techniques (linear and logistic regression, least-square); clustering; dimensionality reduction techniques such as PCA, SVD and matrix factorization. More advanced methods such as generalized linear models, neural networks and Bayesian inference using graphical models are also introduced. The course is self-contained in terms of the necessary mathematical tools (mostly probability) and coding techniques. At the end of the course, students will be able to formalize a ML task, choose the appropriate method in order to tackle it while being able to assess its performance, and to implement these algorithms in Python
Database systems This module provides an introduction to databases and their language systems in theory and practice. The main topics covered by the module include the principles and components of database management systems, the main modelling techniques used in the construction of database systems, and implementation of databases using an object-relational database management system. SQL, the main relational database language, Object-Oriented database systems and future trends (in particular information retrieval and data warehouses) are covered.
Special lectures As a complement to our academic programme, a number of special lectures will be delivered.
The first set of special lectures will be delivered by our industrial stakeholders. These lectures will provide students additional information and exposition to current hot topics in several sectors outside academia, and will give our students the possibility to learn from first hand what kind of problems are currently addressed and solved in industry using networks. These lectures will also constitute a forum that will enable direct communication between students and industry, regarding prospective internships or employment.
The second set of special lectures will be delivered within a network research open day, where our students will be exposed to real research activity which is currently carried out within the Complex Systems & Networks group. Our students will learn about the research atmosphere, research topics, everyday life in academia among other aspects, and will have the possibility to discuss with our research group (academics, postdocs, Phd students) in a relaxed environment.
The majority of our applicants will have an undergraduate degree with first class or upper second class honours (or international equivalent). Offers will typically be made at 2.1 level (upper second class) or equivalent. Students with a good lower second class degree may be considered on an individual basis. In some cases your offer may include additional conditions, such as minimum grades in specified modules, in order to ensure that you are sufficiently qualified for our MSc programmes.
Students applying to this programme should have studied a subject with a substantial mathematical component at the undergraduate level. We welcome those from a variety of relevant disciplines, including mathematics, statistics, physics, engineering, economics and computer science.
Students from outside of the UK help form a global community here at QMUL. For detailed country-specific entry requirements please visit the International section of the QMUL website. If your first language is not English, you must provide evidence of your English language proficiency. Non-native English speakers are required to have minimum of IELTS 6.5 or equivalent. Find out more about our English language entry requirements. If you have not achieved the required English language level yet, you may be eligible to take a Pre-sessional English course, or continue to take English language tests in your country to reach the level needed. Visit: www.sllf.qmul.ac.uk/language-centre/presessionals
Learning and teaching
As a student at Queen Mary, you will play an active part in your acquisition of skills and knowledge. Teaching is by a mixture of formal lectures and small group seminars. The seminars are designed to generate informed discussion around set topics, and may involve student presentations, group exercise and role-play as well as open discussion. We take pride in the close and friendly working relationship we have with our students. You are assigned an Academic Adviser who will guide you in both academic and pastoral matters throughout your time at Queen Mary.
For every hour spent in classes you will be expected to complete further hours of independent study. Your individual study time could be spent preparing for, or following up on formal study sessions; reading; producing written work; completing projects; and revising for examinations.
The direction of your individual study will be guided by the formal study sessions you attend, along with your reading lists and assignments. However, we expect you to demonstrate an active role in your own learning by reading widely and expanding your own knowledge, understanding and critical ability.
Independent study will foster in you the ability to identify your own learning needs and determine which areas you need to focus on to become proficient in your subject area. This is an important transferable skill and will help to prepare you for the transition to working life.
Examinations are held between May and early June on the modules taken. Dissertations are evaluated in September. Successful completion of the MSc programme will result in the award of the degree of MSc in Network Science (possibly with merit or with distinction).
You will also be assessed on a 10,000-word dissertation. This includes the project, a report and a viva. You are encouraged to start thinking about the topic of your final dissertation as soon as possible, a first step is to talk to our lecturers, that are actively developing research in Network Science.
The School of Mathematical Sciences is committed to supporting you through your studies and there is a wide range of support services available both in the School and within QMUL to assist you during your time here.
You will be assigned an Academic Advisor when you enrol with us who will usually stay with you during your time at QMUL. Your Academic Advisor can help to guide you through any academic issues, such as choosing which modules to study.
The School of Mathematical Sciences has a dedicated Student Support Officer to provide you with advice and guidance on any issues that are not primarily academic. The Student Support Officer oversees the i2 - Keepin' It Real initiative which exists to promote and support a positive student experience and is also able to direct you to appropriate QMUL support services. For more information about central student support services, please see Advice and Counselling.
Tuition fees for Home and EU students2018/19 Academic Year
Full time £9,250
Part time £4,650
Tuition fees for International students2018/19 Academic Year
Full time £19,500
Part time £9,750
There are a number of sources of funding available for Masters students.
These include a significant package of competitive Queen Mary University of London (QMUL) bursaries and scholarships in a range of subject areas, as well as external sources of funding.
Queen Mary bursaries and scholarships
We offer a range of bursaries and scholarships for Masters students including competitive scholarships, bursaries and awards, some of which are for applicants studying specific subjects.
Find out more about QMUL bursaries and scholarships.
Alternative sources of funding
Home/EU students can apply for a range of other funding, such as Professional and Career Development Loans, and Employer Sponsorship, depending on their circumstances and the specific programme of study.
Overseas students may be eligible to apply for a range of external scholarships and we also provide information about relevant funding providers in your home country on our country web pages.
Download our Postgraduate Funding Guide [PDF] for detailed information about postgraduate funding options for Home/EU students.
Tel: +44 (0)20 7882 5079
Other financial help on offer at Queen Mary
We offer one to one specialist support on all financial and welfare issues through our Advice and Counselling Service, which you can access as soon as you have applied for a place at Queen Mary.
Our Advice and Counselling Service also has lots of Student Advice Guides on all aspects of finance including:
Tel: +44 (0)20 7882 8717
The balanced mix of delivered analytical and numerical tools, as well as the interdisciplinary nature of the topics taught within the MSc Network Science prepares our students for a large set of careers, including:
- Social Media and Marketing (Data analytics, Social Networks, Digital Media)
- Software and telecommunication companies (Data analysis, Machine Learning)
- Urban planning & Urban mobility
- Government (network security and uncertainty assessment)
- Infrastructure industries (Robustness and vulnerability of infrastructure networks, traffic optimisation)
- Finance sector (Data analysis, network econophysics, financial networks, mathematical modelling)
- Bioinformatics and biomedical sector (biological network analysis)
Network Science is a very active and interdisciplinary field of research, both in academia and in industry. Additionally, we are in the process of setting up a bursary of internships in a selected set of companies for our best students.
We have permanent contact and collaborations with a number of companies in several sectors, including Neo Technology, Microsoft, Telefonica, etc. We are in the process of setting up internships in a selected set of companies for our best students. Please visit this section to keep updated.
One of our sponsors, Neo Technology , a worldwide technological company that provides software products for network databases, will award a yearly Neo4j Networks Prize for the most outstanding student.
As a complement to our academic programme, a number of special lectures will be delivered by our industrial stakeholders. These lectures will provide students additional information and exposition to current hot topics in several sectors outside academia, and will give our students the possibility to learn from first hand what kind of problems are currently addressed and solved in industry using networks. These lectures will also constitute a forum that will enable direct communication between students and industry, regarding prospective internships or employment.
Meetups and workshops
As part of our collaboration with Neo Technology, we strongly encourage our students to attend and participate in the monthly organised meetup workshops, where you will have the possibility to get in touch with professionals working in Network Science and expand your skills and networks.
Neo Technology internship
We have agreed the possibility of developing internships at Neo Technology London. Each application will be studied and assessed in an individual basis.
CNRS Paris internship
We have agreed the possibility of developing internships in Paris (France), within the CNRS's Institut du Cerveau et de la Moelle Épinière, Hôpital de la Pitié-Salpetrière. Each application will be studied and assessed in an individual basis.