What do Facebook, the financial system, Internet or the brain have in common?
"Everything is connected, all is network"
From the underlying skeleton of social relations, the interdependent evolution of our financial system, to the emergent collective computation in the brain, most of the complex systems that appear in society, technology, and nature are ultimately characterised by a nontrivial pattern of inter-relations. This underlying architecture is in turn shaping how information diffuses and spreads, how resilient the system is against attacks or perturbations, or how complex patterns emerge at the systemic level from the aggregation of seemingly simple individuals.
Our MSc Network Science will provide a thorough grounding in the core principles of modelling and analysis of complex and networked systems, along with the principal analytical and numerical methodologies. This will open to students a host of career opportunities in systems and networks modelling industries, spanning the IT, financial, and biomedical sectors, that are now requiring such specialist knowledge and skills.
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. Addressing the description and modelling of the architecture and dynamics of complex systems -systems composed by many interacting units that show collective behaviour- it stands as a new kind of science 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 such as communications and infrastructures (Internet, transportation networks, energy networks, urban mobility), finance (financial risk and systemic instability, financial networks, interbank cross-correlations), marketing and IT (social media, data analytics), public health (epidemic spreading models), or biostatistics and network biology (brain modelling, protein interaction networks, postgenomic era), 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?
This is a pioneering MSc in the UK, a joint programme, taught by our Schools of Mathematical Sciences, and Electronic Engineering and Computer Science, drawing on their strengths in research and teaching in the area of complex networks, mathematical modelling of complex systems, and data mining.
We teach what we know and what we do best. Within the School of Mathematics, the Complex Systems & Networks group is one of the biggest hubs in Network Science within the UK, where we address both fundamental and applied challenges in the mathematical modelling of complex systems with clear societal impact, in collaboration with several industrial stakeholders. Within the School of Electronic Engineering, the Networks group was founded in 1987, and has hugely expanded ever since, bringing their expertise in online social networks, data mining and cloud computing. The coalescence of both groups expertises has fostered the creation of this unique MSc.
More about our two schools
Queen Mary is a member of the prestigious Russell Group of leading UK universities, combining world-class research, teaching excellence and unrivalled links with business and the public sector. The School of Mathematical Sciences has a distinguished history on itself. We have been conducting pioneering mathematical research since the 1950s, and as one of the largest mathematical departments in the UK, with over 50 members of staff, the school can offer diverse postgraduate study opportunities across the field, from pure and applied mathematics, to finance and statistics. Along with the MSc in Network Science, our cohort of postgraduate students specialise in Mathematics and Statistics, Mathematical Finance and Financial Computing. We are one of the UK’s leading universities in the most recent national assessment of research quality, we were placed ninth in the UK (REF 2014) amongst multi-faculty universities. This means that the teaching on our postgraduate programmes is directly inspired by the world-leading research of our academics. Our staff includes international leaders in many areas of mathematical research, and the School is a hive of activity, providing a vibrant intellectual space for postgraduate study.
The School of Electronic Engineering and Computer Science is internationally recognised for their pioneering and ground-breaking research in several areas including machine learning and applied network analysis. This expertise uniquely complements the more theoretical knowledge offered by the School of Mathematical Sciences, providing a well balanced mix of theory and applications and offering a deep and robust programme that combines the foundations of the mathematics of networks with the latest cutting edge applications in real world problems.
Additionally, Queen Mary holds a university-level Bronze Award for the Athena SWAN Charter, which recognises and celebrates good employment practice for women working in mathematics, science, engineering and technology in higher education and research.
You will be able to enjoy the facilities from two schools developing cutting-edge research. Within the Schoolof Mathematical Sciences, The Learning Resource centre has 200 networked PCs and is open to students round the clock, there are dedicated workstations for postgraduate students. You willhave access to specialised facilities and equipment, including:
• A large shared office in the Mathematics Building and a dedicated computer network.
• Access to a full range of computing facilities including a High Performance Computing Unit.
• Our library has about 8,000 mathematical books and subscribes to a large number of mathematical journals. There is also access to substantial numbers of electronic journals.
As a complement, the School of Electronic Engineering and Computer Science offers taught postgraduate students their own computing laboratory. MSc students have exclusive use of the top floor in our purpose-built, climate controlled, award winning Informatics Teaching Laboratory (ITL) outside of scheduled laboratory sessions. The ITL hosts over 250 state-of-the-art PCs capable of multimedia production and several laser printers. In addition, there are video conference facilities, seminar rooms, and on-site teaching services and technical support. There are also a number of breakout spaces available to students with full wi-fi access allowing you use your own mobile devices.
The ITL is primarily used for taught laboratory sessions and regularly hosts research workshops and drop-in lab facilities. For postgraduate students on taught and research degrees there are specialist laboratories to use for carrying out research. Our augmented human interaction (AHI) laboratory combines pioneering technologies including full-body and multi-person motion capture, virtual and augmented reality systems and advanced aural and visual display technologies. We also have specialist laboratories in multimedia, telecommunication networks; and microwave antennas.
You will also have access to Queen Mary’s comprehensive libraries, including the Postgraduate Reading Room, and The British Library can also be accessed as a research resource.
Find out more about this course from our academics and current students by logging in for our next virtual open day. Click the button, below to register.
Programme structure The 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.
Full-time Undertaking a masters programme is a serious commitment, with weekly contact hours being in addition to numerous hours of independent learning and research needed to progress at the required level. When coursework or examination deadlines are approaching independent learning hours may need to increase significantly. Please contact the course convenor for precise information on the number of contact hours per week for this programme.
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.
|COMPULSORY MODULES||OPTIONAL MODULES|
|Graphs and Networks (S1)||Machine Learning (S1)|
|Research Methods in Mathematical Sciences (S1)||Data Mining (S1)|
|Topics in Scientific Computing (S1)||Dynamical Systems (S1)|
|Processes on Networks (S2)||Digital Media and Social Networks (S2)|
|Dissertation (S2)||Computational Statistics (S2)|
|Financial Programming (S2)|
|Database Systems (S2)|
|Complex Systems (S2)|
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.
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.
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.
Computational statistics 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.
Financial programming 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.
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.
We constantly work to improve our programme
We are constantly working to improve our academic curriculum in order to provide our students the best possible programme. In an effort to cope with the intrinsic interdisciplinary nature of Network Science, we will be including additional, trandisciplinary modules to our programme in the near future. Please visit this webpage regularly to be updated!
While being an interdisciplinary topic, Network Science requires some necessary mathematical background. We therefore welcome applications from highly motivated top students around the world with keen and genuine interest in interdisciplinary science that have received some mathematical training in their undergraduate studies. The normal entry requirement for the MSc Network Science is the equivalent of a British first or good second class honours degree in a subject with a substantial mathematical component: for example, mathematics, statistics, physics, computer science, engineering, or economics.
In addition, the undergraduate modules you have taken must provide sufficient background to enable you to take an appropriate selection of our MSc modules. We will assess your suitability for the programme individually. Please be aware that the Admissions Tutor may require further information from you regarding your mathematical background. For further questions please contact us (firstname.lastname@example.org).
Students from outside of the UK help form a global community here at Queen Mary. 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 details on our English language entry requirements here.
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 students
Full time £8,700
Tuition fees for International students
Full time £17,450
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:
- Public Health (Epidemiology)
- Urban planning & Urban mobility
- Infrastructure industries (Robustness and vulnerability of infrastructure networks, traffic optimisation)
- Software and telecommunication companies (Data analysis, Machine Learning)
- Social Media and Marketing (Data analytics, Social Networks, Digital Media)
- Bioinformatics and biomedical sector (biological network analysis)
- Finance sector (Data analysis, network econophysics, financial networks, mathematical modelling)
- Government (network security and uncertainty assessment)
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 a bursary of 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.