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Bielikh, O. (2017) *Generation of Random Graphs for Graph Theory Analysis Applied to the Study of Brain Connectivity*.

** BibTeX **

@mastersthesis{

Bielikh2017,

author={Bielikh, Oleksii},

title={Generation of Random Graphs for Graph Theory Analysis Applied to the Study of Brain Connectivity},

abstract={One of the current frontiers in neurosciences is to understand brain connectivity
both in healthy subjects and patients. Recent studies suggest that brain connectivity
measured with graph theory is a reliable candidate biomarker of neuronal
dysfunction and disease spread in neurodegenerative disorders. Widespread abnormalities
in the topology of the cerebral networks in patients correlate with a higher
risk of developing dementia and worse prognosis.
In order to recognize such abnormalities, brain network graph measures should be
compared with the corresponding measures calculated on random graphs with the
same degree distribution. However, creating a random graph with prescribed degree
sequence that has number of nodes of magnitude of 105 is a recognized problem.
Existing algorithms have a variety of shortcomings, among which are slow run-time,
non-uniformity of results and divergence of degree distribution with the target one.
The goal of this thesis is to explore the possibility of finding an algorithm that can
be used with very large networks. Multiple common algorithms were tested to check
their scaling with increasing number of nodes. The results are compared in order
to find weaknesses and strengths of particular algorithms, and certain changes are
offered that speed up their runtimes and/or correct for the downsides. The degree
distributions of the resulting random graphs are compared to those of the target
graphs, which are constructed in a way that mimics some of the most common
characteristics of brain networks, namely small-worldness and scale-free topology,
and it is discussed why some of the models are more appropriate than others in
this case. Simulations prove that the majority of algorithms are vastly inefficient
in creating random large graphs with necessary limitations on their topology, while
some can be adapted to showcase to a certain extent promising results.},

year={2017},

note={34},

}

** RefWorks **

RT Generic

SR Electronic

ID 256343

A1 Bielikh, Oleksii

T1 Generation of Random Graphs for Graph Theory Analysis Applied to the Study of Brain Connectivity

YR 2017

AB One of the current frontiers in neurosciences is to understand brain connectivity
both in healthy subjects and patients. Recent studies suggest that brain connectivity
measured with graph theory is a reliable candidate biomarker of neuronal
dysfunction and disease spread in neurodegenerative disorders. Widespread abnormalities
in the topology of the cerebral networks in patients correlate with a higher
risk of developing dementia and worse prognosis.
In order to recognize such abnormalities, brain network graph measures should be
compared with the corresponding measures calculated on random graphs with the
same degree distribution. However, creating a random graph with prescribed degree
sequence that has number of nodes of magnitude of 105 is a recognized problem.
Existing algorithms have a variety of shortcomings, among which are slow run-time,
non-uniformity of results and divergence of degree distribution with the target one.
The goal of this thesis is to explore the possibility of finding an algorithm that can
be used with very large networks. Multiple common algorithms were tested to check
their scaling with increasing number of nodes. The results are compared in order
to find weaknesses and strengths of particular algorithms, and certain changes are
offered that speed up their runtimes and/or correct for the downsides. The degree
distributions of the resulting random graphs are compared to those of the target
graphs, which are constructed in a way that mimics some of the most common
characteristics of brain networks, namely small-worldness and scale-free topology,
and it is discussed why some of the models are more appropriate than others in
this case. Simulations prove that the majority of algorithms are vastly inefficient
in creating random large graphs with necessary limitations on their topology, while
some can be adapted to showcase to a certain extent promising results.

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/256343/256343.pdf

OL 30