About Us

Center For Complex Networks & Social Data Science (CCNSD)

The real world is made up of a large number of interacting agents, whose aggregate behavior is nonlinear. However, most appreciated data-driven approaches to study complex systems try to infer the complex interaction network of the system agents, neglecting the cause and consequences of the macroscopic collective modes of the system. We reveal the dormant capacity of the macroscopic information that can be extracted from the interaction network.

For decades, disciplines such as Physics, Mathematics, Computer Science, Psychology, Sociology, and Neuroscience were known as different and independent fields of Science. These days, however, thanks to the huge amount of available data and efficient techniques for tackling the relevant challenges this view has been changed. Computer Scientists and Statistician know how to handle big data and Physics have a lot of experiences working with systems consisted of many particles or agents. Meanwhile, Psychologists and Neuroscientists have questions which are interesting for the former scientists!

Social Media is a successful case which scientists from different disciplines are cooperatively studying it with skills combined from distinct backgrounds. Social Cognition, is trying to solve the problems of our modern society with respect to the perspective of culture, market, business and management with the tools of different disciplines. Problems, which historically were dedicated to special fields are now common topics of research amount different disciplines. Internet, especially social media, is playing a crucial role in this new scenario of doing interdisciplinary research.

Our Line of Research:

  • Collective Behavior in Financial Markets, Technology, and Human Networks
  • Social Networks Dynamics (Aged networks, Dark, and non-transparent networks)
  • Complexity Economy & Econophysics (Crises, Systemic Risk, Portfolio & Horizon Investment)
  • Stochastic Processes (Non-Gaussian & several coupled series, Multi-fractal)
  • Machine Learning
  • ‌Percolation Processes and Structural Phase transitions
  • Multilayer Networks
Graduate Students of CCNSD, Feb 2019

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