
## References

[1] S. Wasserman, K. Faust, Social network analysis: Methods and applications, Cambridge Univ. Press, 1994.

[2] R. Guimera, L. Amaral, Functional cartography of complex metabolic networks, Nature 433 (7028) (2005) 895–900.

[3] Y. Loewenstein, E. Portugaly, M. Fromer, M. Linial, Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space, Bioinformatics 24 (13)
(2008) i41 - i49.

[4] Z. Chen, Y. He, P. Rosa-Neto, J. Germann, A. Evans, Revealing modular architecture of human brain structural networks by using cortical thickness from **MRI, Cerebral Cortex 18 (10) (2008)** 2374 - 2381.

[5] W. Crum, Spectral Clustering and Label Fusion For 3D Tissue Classification: Sensitivity and Consistency Analysis, in: Proc. Med. Im. Underst. Anal., Dundee, Scotland, 2008.

[6] M. Girvan, M. E. J. Newman, Community structure in social **and biological networks,**
Proc. Natl. Acad. Sci. 99 (12) (2002) 7821 - 7826.

[7] M. Rosvall, C. T. Bergstrom, Maps of random walks on complex networks reveal community structure, Proc. Natl. Acad. Sci. 105 (4) (2008) 1118 - 1123.

[8] S. Lafon, A. Lee, Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization, IEEE
Trans. Patt. Anal. Mach. Intel. 28 (9) (2006) 1393 - 1403.

[9] J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Trans. Patt. Anal. Mach. Intel. 22 (8) (2000) 888–905.

[10] C. Fowlkes, S. Belongie, F. Chung, J. Malik, Spectral grouping using the Nystr¨om method, IEEE Trans. Patt. Anal. Mach. Intel. 26 (2) (2004) 214–225.

[11] I. Dhillon, Y. Guan, B. Kulis, Weighted graph cuts without eigenvectors: a multilevel approach, IEEE Trans. Patt. Anal. Mach. Intel. 29 (11) (2007) 1944–1957.

[12] L. Yu, C. Ding, Network community discovery: solving modularity clustering via normalized cut, in: Proc. ACM Wkshp Mining and Learn. with Graphs, 2010.

[13] A. Sinclair, M. Jerrum, Approximate counting, uniform **generation and rapidly mixing Markov**
chains, Info. Comp. 82 (1) (1989) 93 - 133.

[14] W. Donath, A. Hoffman, Algorithms for partitioning of graphs and computer logic based on eigenvectors of connection matrices, IBM Tech. Disc. Bull. **15 (3) (1972) 938 - 944.**
[15] A. Pothen, H. Simon, K.-P. Liou, Partitioning sparse matrices with eigenvectors of graphs, SIAM J. Matrix Anal. App. 11 (1) (1990) 430–452.

[16] B. Hendrickson, R. Leland, A multilevel algorithm for partitioning graphs, in: Proc. ACM
Int. Conf. Supercomp., Barcelona, Spain, 1995.

[17] G. Karypis, V. Kumar, A fast and high quality multilevel **scheme for partitioning irregular**
graphs, SIAM J. Sci. Comp. 20 (1) (1998) 359 - 392.

[18] M. Meila, J. Shi, A random walks view of spectral segmentation, in: Proc. Int. Wkshp Art. Intel. Stat., Key West, FL, USA, 2001.

[19] A. Ng, M. Jordan, Y. Weiss, On spectral clustering: Analysis and an algorithm, in:
Proc. Adv. Neur. Inf. Proc. Sys., Vancouver, BC, Canada, 2001.

[20] D. Yan, L. Huang, M. I. Jordan, Fast approximate spectral clustering, in: Proc. ACM
Int. Conf. Knowl. Disc. Data Mining, Paris, France, 2009.

[21] E. Sharon, A. Brandt, R. Basri, Fast multiscale image segmentation, in: Proc. IEEE
Conf. Comp. Vis. Patt. Recog., Hilton Head, SC, USA, 2000.

[22] C. Ding, X. He, H. Zha, M. Gu, H. Simon, A min-max cut algorithm for graph partitioning and data clustering, in: Proc. IEEE Int. Conf. Data Mining, San Jose, CA, USA, 2001.