“Quantification of Network Dissimilarities and its Practical Implications”
Panos Pardalos, Department of Systems Engineering, University of Florida, USA. Director of the Center for Applied Optimization.
In this lecture, we analyze a novel measure that quantifies network dissimilarities by
comparing its performance with other well-known tools. The efficacy of this measure,
based on Information Theory, depends on the use of rich information extracted from the
graphs. We show here that the measure has promising implications in several research
areas that include, bioinformatics, climate dynamics, percolation in networks, network
robustness and model selection. We perform extensive computational experiments on real
and artificial networks. Future research directions, which include applications to
multiplex settings, will also be discussed.
This is joint work with T. Schieber, M.G. Ravetti, and L. Carpi