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Network modelling of complex systems: from correlation graphs to information hypergraphs

GA21-17211S [Registered results] [WWW] 2021 - 2024

Development of methods for effective description of complex systems is a growing area of interdisciplinary research at the junction of cybernetics, informatics, mathematics and theoretical physics, with application to a range of scientific disciplines including neuroscience, sociology, economics, genetics, and ecology. One of the key problems is the robust characterization of the structure of interactions within a system based on the multivariate time series. A common method for system representation is using correlation matrix of the variables, treated as graph and studied using graph-theoretical metrics, in comparison with random graphs of matched size and density. Recent results show multiple issues of such approach: correlation matrices do not capture faithfully the interactions, neglect higher-order dependences, insufficiently capture the predictive power in multivariate nonlinear models and lead to a bias in key graph metrics. We shall move beyond the state-of-art and our recent results towards a more general and robust complex system characterization. The main aim of the project is to bring theoretical and methodological advances in complex network research by tackling four key challenges outlined in the abstract.