Stefano Boccaletti "Parenclitic networks: how to uncover new functions and structural information from data"
Abstract: In this talk I will describe a novel method to represent time independent, scalar data sets as complex networks, and show (as an illustrative example) how it applies to biological data (gene expression in the response to osmotic stress of Arabidopsis thaliana). The proposed network representation allow to identify the most important features distinguishing an individual from a class: for the case of the plant response it turns out to be the nodes with highest centrality in appropriately reconstructed networks, called parenclitic networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure.
Stefano Boccaletti is a senior researcher in the Institute of Complex Systems in Florence. He is a specialist in statistical and nonlinear physics, nonlinear optics and complex systems sciences with applications to biology, medicine, social sciences and other areas. He contributed several influential papers studying the synchronization effect in complex networks, including the now standard way of their classification. His monograph "Complex Networks: Structure and Dynamics" is among the highest ever cited publications in the field of complex networks and applications.
Nelly Litvak "Current research directions in complex networks: an informal survey"
Nelly Litvak is a professor in Algorithms for Complex Networks with background in Applied Probability and Stochastic Operations Research. Her main research interests are in large networks such as on-line social networks and the World Wide Web, randomized algorithms, and random graphs, and since recently prediction for networks using machine learning. She is also known as an excellent lecturer and mathematics popularizer.