PhD Program in Network Science at CEU
Doctor of Philosophy in Network Science
Program level: Post-graduate (Doctoral)
Degree awarded: PhD
Unit: Department of Network and Data Science
Program registration: Program approved and registered by the New York State Education Department
Program length: 3-6 years
Type of degree: CEU
CEU credits: 90
ECTS credits: 180.00
Start of the program: September
Head of Department: János Kertész
Email: dnds@ceu.edu
PROGRAM URL: Doctor of Philosophy in Network Science
CEU Application Details
Program Description
The PhD program in Network Science is a research-oriented program that provides the only PhD degree in this field in Europe. Network science provides essential tools to study complex systems including society online and offline, the economy or urban traffic. Accordingly, the program provides hands-on experience with large datasets characterizing those systems and the skills needed to analyze them. At the same time, network science is a rapidly developing new discipline with ample opportunities to do fundamental research. Within the PhD program there are possibilities to carry out research either in applied or in theoretical-methodological directions.
Application oriented, data driven research in Network Science | THEORETICAL AND METHODOLOGICAL FOUNDATIONS OF NETWORK SCIENCE |
| Many of today’s pressing societal problems, ranging from climate change and migration to political instability due to populism, stem from the emerging complexity of the underlying systems. The explosion of empirical data relevant to these problems offers rich opportunities for research using concepts and tools from Network Science. Possible directions include: | The theoretical framework of Network Science is comprised of an interdisciplinary combination of statistical physics, mathematics, and computer science. Despite considerable progress in the last decades, there remains open challenges in many directions, including: |
| Financial networks: Interbank networks, multilayer economic interactions, systemic risk assessment and forecasting, stress testing, financial data analysis with network tools, blockchain and collaborative credit systems, spreading of innovations. | Processes on networks: epidemic spreading and social contagion, opinion and cultural dynamics, evolutionary game theory, random walks and diffusion, synchronization, rank dynamics, control, cascades. |
| Psychological and knowledge networks: Co-occurrence of psychological features, network of psychological disorders, exploring and mapping knowledge networks, collaborative knowledge production, science of science. | Mesoscopic structures: Community discovery, generative models of network structure and their statistical inference. |
| Networks in political science: Relation between corruption risk and social structure, detection of collusion with network tools, algorithmic bias and political segregation, role of ‘bots’ in spreading fake news, rumor spreading and opinion dynamics. | Network measurement and reconstruction: Error assessment and statistical network analysis, network recovery from dynamics and indirect measurements. |
| Urban networks: Multimodal transportation systems, mobility networks, bicycle networks and sustainability, infrastructural networks and smart cities. | Temporal networks: Representations of time-varying networks, identification of relevant time-scales, dynamical processes on temporal networks, mechanisms and modelling of network dynamics. |
| Social networks: Collaboration networks, network anthropology, creativity in teams, analysis of online social networks, homophily and social influence. | Multilayer networks: multiplex and interconnected networks, intertwined multiplex dynamical processes, reducibility of multilayer networks. |
| Socioeconomic networks: Socioeconomic inequalities, social stratification, segregation in networks, inference of socioeconomic status, coupled datasets. | Other higher-order models of networks: Structure and dynamics of networks beyond pairwise interactions, physical networks, network embedding. |
The core courses will provide students with the necessary background to develop their research program in any of the main areas above. The specific topics mentioned above are not exhaustive, and students are encouraged to creatively formulate their own original research program.
During their research work, students will participate in international research projects and by the end of their PhD studies they will develop into independent researchers, with the ability to contribute to network science in its entire scope.
Students admitted into CEU doctoral programs are eligible to receive the CEU Doctoral Fellowship for up to three years and to an optional write up grant of six additional months. Numerous additional funding opportunities exist, including travel grants.
Sample Courses for the Doctoral Program
Core courses
- Fundamental Ideas in Network Science
- Structure and Dynamics of Complex Networks
- Statistical Methods in Network Science
- Social Networks
- Data Mining and Big Data Analytics
Optional courses
- Scientific Python
- Agent Based Models
- Data and Network Visualization
- Introduction to Computational Social Science
- Dynamical Systems on Networks
Entry Requirements for the Doctoral Program (Program-Specific Requirements)
Apart from general CEU admission requirements, we expect applications from candidates with a Master’s degree in one of a broad range of related disciplines (including physics, mathematics, computer science, sociology, political science and economics), who have an interest in network science and/or its applications.
The PhD program is open for students with a wide variety of backgrounds. Presently we have students with MA/MSc in math, physics, sociology, psychology, architecture, economics and political science. The Program is strongly interdisciplinary with a special emphasis on quantitative methods and data-oriented research. Those who have weaker math backgrounds will have to participate in a pre-session course and will need some additional effort.
We do NOT request a GRE test.
In addition to the items of the CEU checklist, a STATEMENT OF PURPOSE (previously “motivation letter”) is an important part of the application.
The Statement of Purpose should make clear:
- why you are interested in studying network science at CEU;
- your knowledge of network science literature;
- your openness towards and possible experience with mathematical methods and programming;
- what is the special field of research you are particularly interested in and what kind of plans you have in that direction;
- what kind of studies and activities you have carried out that are related to your plans.
The Statement of Purpose should not be longer than 1500 words.