Courses

Course code: CNSC 6001Course DescriptionComplex systems are abundant: The society, the economy, the financial system, food webs, energy supply systems are just some examples. The recent development of information technology opened unprecedented opportunities in studying them. On the one hand due to making available...
Instructor: Rosario N. Mantegna, János Török
Credits: 2.0
Basic concepts and theorems are presented. Some significant applications are analyzed to illustrate the power and the use of combinatorial optimization. Special attention is paid to algorithmic questions.The goals of the course:One of the main goals of the course is to introduce students to the most important results...
Instructor: Ervin Győri
Credits: 3.0
Prerequisites: You need to be proficient with Python to take this course – read the “to satisfy the prerequisite” section belowCourse schedule: this course will take place twice a week during the second half of the term, starting on November 2, 2017.Course Level: Master and PhDOffice: 609 Nador 11Office hours: TBA or...
Instructor: Roberta Sinatra
Credits: 2.0
Course code: CNSC 6006Course Instructor: Prof. Roberta Sinatra, sinatrar@ceu.eduOffice: N11 609Office hours: TBA or by appointmentIMPORTANT: During the first class, we will hand out a test to check the prerequisites among the students. Those that do not reach the minimum threshold will not be able to take the course,...
Instructor: Roberta Sinatra
Credits: 2.0
ECONOMIC AND SOCIAL NETWORKS SYLLABUSFull description: Social networks affect many economic transactions. They transmit information about job opportunities, affect the trade of goods and services, influence how diseases spread, which products we buy, how we vote, whether we become criminals, which technologies we...
Instructor: Adam Szeidl
Credits: 2.0
This course presents network concepts from the two distinct perspectives of economics and network science. It will provide a background covering both economics modeling of networks and network science modeling of economic systems. The course presents problems where network aspects are instrumental to achieve a better...
Instructor: Rosario N. Mantegna
Credits: 2.0
EMPIRICAL FINANCE SYLLABUSLevel:  Doctoral Course Status:  ElectiveFull description: Background and overall aim of the course.This course aims to describe the process of price formation of a financial asset starting from the analysis of the empirical regularities (often called as "stylized facts") observed in the time...
Instructor: Rosario N. Mantegna
Credits: 2.0
Course code: CNSC 6000Level: DoctoralCourse Status: MandatoryBackground and overall aim of the course:Networks are ubiquitous. Economic trade, social relationships, terrorist organizations or biochemical reactions – all span networks. Network science has gone through a spectacular development recently. The data deluge...
Instructor: János Kertész
Credits: 4.0
TOPICS IN COMBINATORICS SYLLABUSLevel:  Doctoral Course Status:  ElectiveFull description: Brief introduction to the course:More advanced concepts, methods and results of combinatorics and graph theory. Main topics: (linear) algebraic, probabilistic methods in discrete mathematics; relation of graphs and hypergraphs;...
Instructor: Ervin Győri
Credits: 3.0
Brief introduction to the course:While probability theory describes random phenomena, mathematical statistics teaches us how to behave in the face of uncertainties, according to the famous mathematician Abraham Wald. Roughly speaking, we will learn strategies of treating randomness in everyday life. Taking this course...
Instructor: Marianna Bolla
Credits: 3.0
Level:  DoctoralCourse Status:  ElectiveFull descriptionBackground and overall aim of the courseIntroduces network science and the set of tools used to understand complex networks emerging in social and economic systems. Focuses on the empirical study of real networks, with examples from computer science (World Wide...
Credits: 1.0
Course code: CNSC 6008Level: DoctoralCourse Status: ElectiveCourse Instructor: Prof. Roberta Sinatra, sinatrar@ceu.eduOffice hours: TBA or by appointmentBrief introduction to the courseNetwork science is fundamentally a computational science. This course will provide algorithms and tools to generate network data and...
Instructor: Roberta Sinatra
Credits: 3.0
RANDOM GRAPHS AND NETWORK SIMULATIONS SYLLABUSLevel: DoctoralCourse Status: ElectiveFull description: Background and overall aim of the course:The success of the new science of networks is partly due to the realization of universal features in a wide range of applications,  but also due to very successful simple...
Instructor: János Kertész
Credits: 2.0
MATH 5016Level: DoctoralCourse Status: ElectiveHost Unit: Department of Mathematics and its ApplicationsCo-hosting Unit(s): Center for Network Science (CNS)Course coordinator: Roberta Sinatra, SinatraR@ceu.eduBrief introduction to the courseThis course will provide a comprehensive, fast - paced introduction to...
Instructor: Roberta Sinatra
Credits: 3.0
Simulation methods (Department of Mathematics) Level:  Doctoral or MA 2nd yearCourse Status:  ElectiveFull description: Brief introduction to the courseThe aim of the course is to give an introduction to simulation methods. The illustrative tasks will be from statistical physics. The main simulation techniques as...
Instructor: János Kertész
Credits: 2.0
SOCIAL NETWORKS SYLLABUSCNSC 6011Level:  DoctoralCourse Status:  Mandatory Full description: General scopeThe aim of this course is to give an overview of the key ideas of network science from a social science perspective.  The concept of networks has come to pervade modern society, as we routinely make use of online...
Instructor: Balazs Vedres
Credits: 4.0
Level:  DoctoralCourse Status: ElectiveCourse descriptionThe increasing volume and nature of big datasets in business, economics, social and political sciences call for more complex and sophisticated data mining tools. The complex systems monitored by big databases are successfully described in terms of networks. In...
Instructor: Rosario N. Mantegna
Credits: 4.0
Course code: CNSC 6003Level:  Doctoral or MACourse Status:  Electivee-learning.ceu.eduOffice hours: upon agreementCourse DescriptionVery few processes are completely deterministic – an element of randomness is almost always present. The adequate mathematical framework to treat this randomness  is the theory of...
Instructor: János Kertész
Credits: 2.0
Course code: CNSC 6007DescriptionThe aim of this course is to deepen the knowledge of students in networks science, to get acquainted with some important specific problems at the frontier of research and get skills to work with present day literature.The bulk of the course will be provided in lectures. There will be...
Instructor: János Török
Credits: 2.0
Course code: CNSC 6007Level:  Doctoral Course Status:  MandatoryCourse DescriptionThe success of the new science of networks is partly due to the realization of universal features in a wide range of applications,  but also due to very successful simple models. The following questions will be discussed: How to identify...
Instructor: János Kertész
Credits: 2.0
Course code: CNSC 6015Our current approach to success is driven by the belief that predicting exceptional impact requires us to detect extraordinary ability. Despite the long-standing interest in the problem, even experts remain notoriously bad at predicting long-term impact. Success becomes suddenly predictable,...
Credits: 2.0
LARGE SPARSE GRAPHS, GRAPH CONVERGENCE AND GROUPS SYLLABUSLevel:  DoctoralCourse Status:  ElectiveFull description: Brief introduction to the course:A family of finite graphs is sparse, if the number of edges of a graph in the family is proportional to the number of its vertices. Such families of graphs come up...
Instructor: Miklós Abert
Credits: 3.0