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Title:Automatic reconstruction of complex dynamical networks : doctoral dissertation
Authors:ID Grau Leguia, Marc (Author)
ID Levnajić, Zoran (Mentor) More about this mentor... New window
ID Andrzejak, Ralph Gregor (Mentor) More about this mentor... New window
ID Ženko, Bernard (Comentor)
Files:.pdf DR_2019_Marc_Grau_Leguia.pdf (6,96 MB)
MD5: 307935369B2AEB8C2D4C2B9325781238
 
Language:English
Work type:Doctoral dissertation
Typology:2.08 - Doctoral Dissertation
Organization:FIŠ - Faculty of Information Studies in Novo mesto
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.
Keywords:network reconstruction, simulated annealing, dynamical systems, nonlinear interdependence measure, EEG
Place of publishing:Novo mesto
Place of performance:Novo mesto
Publisher:[M. Grau Leguia]
Year of publishing:2019
Year of performance:2019
Number of pages:XXI, 88 str.
PID:20.500.12556/ReVIS-5578 New window
COBISS.SI-ID:2048577811 New window
UDC:53:517.938(043.2)
Note:Na ov.: Doctoral Dissertation;
Publication date in ReVIS:19.04.2019
Views:2949
Downloads:148
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:19.04.2019

Secondary language

Language:Slovenian
Title:Avtomatska rekonstrukcija kompleksnih dinamičnih omrežij
Abstract:Eden najpomembnejših problemov v znanosti o omrežjih je, kako rekonstruirati strukturo realnega omrežja na osnovi signalov izmerjenih v njenih notranjih enotah. Določitev arhitekture kompleksnih omrežij je ključnega pomena, ne samo za razumevanje njihovega delovanja, ampak tudi za napovedovanje in regulacijo njihovega delovanja. Trenutno znane metode se večinoma osredotočajo na odkrivanje povezav v neusmerjenih omrežjih in pogosto temeljijo na strogih predpostavkah o delovanju sistema, veliko teh metod pa tudi ni mogoče uporabiti v omrežjih z usmerjenimi povezavami. V disertaciji se osredotočimo na rekonstrukcijo usmerjenih omrežij. Predstavimo dve novi metodi za rekonstrukcijo omrežij. Prva predpostavi, da preiskovano omrežje deluje v okviru vnaprej določenega modela, in iskano strukturo omrežja najde na osnovi statističnih povezav med spremenljivkami in njihovimi odvodi ter z uporabo evolucijske optimizacije. Druga metoda ne zahteva nobenih predpostavk o delovanju omrežja in iskano omrežje določi le na osnovi podatkov z uporabo nelinearne mere medsebojne odvisnosti spremenljivk. Slednja metoda je uporabna za rekonstrukcijo strukture usmerjenih omrežij kaotičnih Lorenzovih oscilatorjev z različnimi stopnjami sklopitve in različno gostoto povezav. Metodo uporabimo tudi za analizo meritev zbranih z večkanalnim elektroencefalografom pri bolniku z epilepsijo. Rekonstruirana funkcionalna možganska omrežja, dobljena s to metodo, se skladajo s trenutnim medicinskim znanjem.
Keywords:rekonstrukcija omrežja, simulirano žarjenje, dinamični sistemi, merilo nelinearne soodvisnosti, EEG


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