| Title: | Automatic reconstruction of complex dynamical networks : doctoral dissertation |
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| Authors: | ID Grau Leguia, Marc (Author)
ID Levnajić, Zoran (Mentor) More about this mentor... 
ID Andrzejak, Ralph Gregor (Mentor) More about this mentor...
ID Ženko, Bernard (Comentor) |
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| Files: |  DR_2019_Marc_Grau_Leguia.pdf (6,96 MB)
MD5: 307935369B2AEB8C2D4C2B9325781238
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| Language: | English |
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| Work type: | Doctoral dissertation |
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| Typology: | 2.08 - Doctoral Dissertation |
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| Organization: | FIŠ - Faculty of Information Studies in Novo mesto
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| Abstract: | A foremost problem in network science is how to reconstruct (infer) the topology of a real network from signals measured from its internal units. Grasping the architecture of complex networks is key, not only to understand their functioning, but also to predict and control their behaviour. Currently available methods largely focus on the detection of links of undirected networks and often require strong assumptions about the system. However, many of these methods cannot be applied to networks with directional connections. To address this problem, in this doctoral work we focus at the inference of directed networks. Specifically, we develop a model-based network reconstruction method that combines statistics of derivative-variable correlations with simulated annealing. We furthermore develop a data-driven reconstruction method based on a nonlinear interdependence measure.
This method allows one to infer the topology of directed networks of chaotic Lorenz oscillators for a subrange of the coupling strength and link density. Finally, we apply the data-driven method to multichannel electroencephalographic recordings from an epilepsy patient. The functional brain networks obtained from this approach are consistent with the available medical information. |
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| Keywords: | network reconstruction, simulated annealing, dynamical systems, nonlinear interdependence measure, EEG |
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| Place of publishing: | Novo mesto |
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| Place of performance: | Novo mesto |
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| Publisher: | [M. Grau Leguia] |
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| Year of publishing: | 2019 |
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| Year of performance: | 2019 |
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| Number of pages: | XXI, 88 str. |
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| PID: | 20.500.12556/ReVIS-5578 |
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| COBISS.SI-ID: | 2048577811 |
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| UDC: | 53:517.938(043.2) |
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| Note: | Na ov.: Doctoral Dissertation;
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| Publication date in ReVIS: | 19.04.2019 |
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| Views: | 2949 |
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| Downloads: | 148 |
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