GMSA 2018 - Special Session on Graphical model selection and applications

Organizers:
Dr. Valeriy Kalyagin, Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia
Dr. Mario Guarracino, High Performance Computing and Networking Institute, Italian National Research Council, Naples, Italy
Description:
Graphical models provide a unifying framework for capturing dependencies in complex systems. Graphical models are recognized as a useful tool in many applied fields, such as bioinformatics, communication theory, combinatorial optimization, signal and image processing, information retrieval, stock market network analysis and statistical machine learning. Graphical model selection is a practical problem of identification of the underlying graphical model from observations. The session will be devoted to theoretical aspects and practical algorithms for graphical model selection and its applications. Estimating the graph structure given a set of observations at the nodes is very common in many fields and in particular in biology, where the complexity of processes and functions are widely modeled by networks. From protein interaction to metabolic pathways, from gene regulatory circuits to brain connectomes, networks have sizes that range from few thousands to many trillions vertices. From their analysis, we can obtain more insights in complex questions, identifying for example their critical points, robustness and modularity. In this session, we will address some of the recent advances on graphical model selection, that can find application in different disciplines and applications.
Topics of interest include but are not limited to:
Graphical model selection in bioinformatics
Graphical model selection in communication
Graphical model selection in combinatorial optimization
Graphical model selection in signal and image processing
Graphical model selection in information retrieval
Graphical model selection in market network analysis
Graphical model selection in statistical machine learning
Graphical model selection in gene expression network
Graphical model selection in gene co expression network