Research Program: Complex Networks
Leader: Riste Škrekovski
Duration: 1.1.2013 - 11.2017

Science of complex networks studies the mechanisms of emergence of collective phenomena, arising through the self-organization in systems composed of many interacting entities such as genes, ants or persons. By combining graph theory with experimental insights into real complex systems such as society or the Internet, this young interdisciplinary science contributed fundamental results from understanding of the genetic systems to improving infrastructure networks. Our Research Program involves five directions:

      • development of the novel methods of reconstructing the structure and predicting links in real networks based on the empirical data;
      • detecting the properties of biological networks responsible for their superb functionality and manufacturing bio-inspired methods of network design;
      • defining a unique identification of network complexity by completing presently available set of indices that quantify network structure;
      • analytical and numerical extensions of classical graph optimization problems to large real-world networks;
      • construction of novel models of evolving social networks through bio-inspired paradigms such as ant colonies and evolutionary algorithms.

This interdisciplinary research relies on computationally intensive modeling and simulations, along with methods from machine learning, optimization theory and graph theory.