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Parenclitic networks: How to uncover new functions and structural information from data
In this talk, Stefano Boccaletti will describe a novel method for representing time-independent, scalar datasets as complex networks and show it applied to biological data on the example of gene expression in response to osmotic stress in Arabidopsis thaliana. The proposed network representation allows 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 the 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 was compared with that of the wild-type. This novel representation extends the use of graph theory to datasets hitherto considered to be outside the realm of its application, vastly simplifying the characterization of their underlying structure.
Stefano Boccaletti is a senior researcher at the Institute for Complex Systems under the Italian National Research Council. He is a specialist in statistical and nonlinear physics, nonlinear optics, and complex systems sciences with applications in biology, medicine, social sciences, and other fields. He contributed several influential papers on the synchronization effect in complex networks, including the now standard way of classifying them. His monograph “Complex Networks: Structure and Dynamics” is among the the most frequently cited publications in the field of complex networks and applications.
Shortly after the workshop, Boccaletti will deliver a series of four lectures titled “Complex Networks: Introduction and Applications.” The first of them starts Nov. 18 at 1:55 p.m. in Room 123 of the Main Building.
Current research directions in complex networks: An informal survey
Nelly Litvak is a professor of algorithms for complex networks with a background in applied probability and stochastic operations research. Her main interests are large networks, such as online social networks and the World Wide Web, randomized algorithms, and random graphs. She has recently became engaged in prediction for networks using machine learning. Litvak is known as an excellent lecturer and a popularizer of mathematics.
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