Statistical inference for complex network discovery and feature extraction
Dates: from Jan. 1, 2010 to June 30, 2014
Funder: MINECO (Spain)
Project id: FIS2010-18639
Total Funding: 45,000€
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In complex systems, individual components interact with each other giving rise to complex networks of interactions that are neither totally regular nor totally random. Although during the last decade significant progress has been made in the study of complex networks, we are still far from the ultimate goals of: (i) characterizing real-world complex networks; (ii) understanding the precise mechanisms responsible for the observed topology; and (iii) evaluating the impact of the structure of the network on the dynamics of the system.
The two main impairments to achieve these goals are: (i) most network data are very unreliable, that is, for most systems there is uncertainty as to what is the real structure of the network; and (ii) we lack the tools to extract the relevant information contained in the structure of networks, and to evaluate the impact of network structure on a system’s dynamics.
The general aims of the project are to advance in the solution of the two challenges described above by developing theoretical tools for network reliability assessment and feature extraction, and to apply these tools to biological and socio-economic systems of interest. We are going to approach these problems using statistical physics and group-based models of complex networks. More specifically, we propose to develop a statistical mechanics framework for assessing network reliability and for network discovery (that is, to infer the whole network from a partial observation of it). We will also develop a framework for network feature extraction based on the identification of the most relevant network partitions and of those connections that deviate from the predominant patterns. Finally, we will apply the theoretical tools developed within the project to: (i) biological systems and (ii) socio-economic systems.
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