Research Focus QNetS
The QCSSC has been awarded support from the LMU Center for Advanced Studies (CAS) for a Research Focus called “Quantitative Network Science (QNetS)” whose members are drawn from (financial) mathematics, (bio)statistics, computer science, neurobiology, medicine, and other disciplines. Activities within QNetS include
- Public Lectures
- Fellows Program
Quantitative Network Science (QNetS) – Summary
Quantitative network science, the science of studying and analysing complex networks, has become increasingly important and has grown significantly in recent years. Network science and network data analysis are not confined to any single scientific discipline as networks occur in diverse areas of science. Networks provide an abstract way of describing relationships and interactions between elements of complex and heterogeneous systems. For example, the World Wide Web can be represented as a network whose vertices are the HTML documents, connected by the hyperlinks that point from one page to another. On a different level, our nervous system forms a large network, whose vertices are the neurons and nerve cells, which are connected by axons. Complex networks are also considered in social and economic sciences. There the vertices represent (specific groups of) individuals or entities, and the edges describe social (or some other type of) interaction between them. Yet another example of the use of networks is in information visualization and visual analytics in order to discover unexpected patterns in network data.
While the scientific disciplines in which networks occur are diverse, the needs for analysis are similar and may be dealt with by using the same or similar quantitative methods and models. These include methods and theories ranging from mathematical graph theory and statistical network models to visualization techniques in computer science. The Research Focus QNetS intends to bundle the different activities within network science at the LMU Munich and brings together mathematicians, statisticians, and computer scientists with empirical scientists from a wide range of disciplines in order to advance the field of quantitative network science.