Complex Brain Networks: A Graph-Theoretical Analysis

245

Bibliography

[1] H. Tang, G. Ma, Y. Zhang, et al., “A comprehensive survey of complex

brain network representation,” Meta Radiology, vol. 1, no. 3, p. 100046,

2023.

[2] S. Simpson, F. Bowman, and P. J. Laurienti, “Analyzing complex func-

tional brain networks: Fusing statistics and network science to understand

the brain,” Stat Surv, no. 7, pp. 1–36, 2013.

[3] K. Erciyes, Distributed Graph Algorithms for Computer Networks.

Springer Computer Communications and Networks Series, 2013.

[4] D. Watts and S. Strogatz, “Collective dynamics of ’small-world’ net-

works,” Nature, vol. 69, no. 6684, pp. 440–442, 1998.

[5] A. Barabasi and R. Albert, “Emergence of scaling in random networks,”

Science, no. 286, pp. 509–512, 1999.

[6] K. Erciyes, Distributed and Sequential Akgorithms for Bioinformatics.

Springer Computational Biology Series, 2015.

[7] M. Newman and M. Girvan, “Finding and evaluating community struc-

ture in networks,” Phys Rev, vol. 69, no. 2, p. 026113, 2004.

[8] O. Sporns and R. Betzel, “Modular Brain Networks,” Annu Rev Psychol,

no. 67, pp. 613–640, 2016.

[9] V. Blondel, J. Guillaume, R. Lambiotte, et al., “Fast unfolding of commu-

nities in large networks,” Journal of Statistical Mechanics: Theory and

Experiment, vol. 2008, no. 10, p. P10008, 2008.

[10] D. Bassett, M. Porter, N. Wymbs, et al., “Robust detection of dynamic

community structure in networks,” Chaos, vol. 23, no. 1, 2013.

[11] Z. Guo, X. Zhao, L. Yao, et al., “Improved brain community structure

detection by two-step weighted modularity maximization,” PLoS One,

vol. 18, no. 12, 2023.

[12] R. Craddock, G. James, P. Holtzheimer, et al., “A whole brain fMRI atlas

generated via spatially constrained spectral clustering,” Human Brain

Mapping, vol. 33, no. 8, pp. 1914–1928, 2012.

[13] K. Andersen, K. Madsen, H. Siebner, et al., “Non-Parametric Bayesian

Graph Models Reveal Community Structure in Resting State fMRIs,”

Neuroimage, no. 100, pp. 301–35, 2014.