Abstract
The community structure detection problem in weighted networks is tackled with a new approach based on game theory and extremal optimization, called Weighted Nash Extremal Optimization. This method approximates the Nash equilibria of a game in which nodes, as players, chose their community by maximizing their payoffs. After performing numerical experiments on synthetic networks, the new method is used to analyze functional connectivity networks of the brain in order to reveal possible connections between different brain regions. Results show that the proposed approach may be used to find biomedically relevant knowledge about brain functionality.
Keywords
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- 1.
Such projects include the BRAIN Initiative (http://www.braininitiative.nih.gov/, April, 2016) and the European Human Brain Project (https://www.humanbrainproject.eu/, April, 2016).
- 2.
By using the code available at https://sites.google.com/site/andrealancichinetti/software, accessed May, 2015.
- 3.
See http://fcon_1000.projects.nitrc.org/indi/ACPI/html/ for details.
- 4.
One ROI (Basal Ganglia 4) did not include meaningful measurement for any of the 126 subjects, therefore we ignored this ROI in the subsequent analysis.
- 5.
We note that in the brain research community, the phrases default mode network and salience network are used to refer to two specific sets of strongly interconnected regions of the brain. Therefore, the default mode network and the salience network are communities according to the terminology used throughout this paper.
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Acknowledgment
K. Buza was supported by the grant of the National Research, Development and Innovation Office - NKFIH PD 111710 and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. This work was also supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-2332.
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Lung, R.I., Suciu, M., Meszlényi, R., Buza, K., Gaskó, N. (2016). Community Structure Detection for the Functional Connectivity Networks of the Brain. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_59
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