U. and V. Luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1017/CBO9780511810633

C. Tsourakakis, Fast Counting of Triangles in Large Real Networks without Counting: Algorithms and Laws, 2008 Eighth IEEE International Conference on Data Mining, 2008.
DOI : 10.1109/ICDM.2008.72

D. Kempe and F. Mcsherry, A decentralized algorithm for spectral analysis, STOC, 2004.
DOI : 10.1145/1007352.1007438

URL : http://www-static.cc.gatech.edu/~mihail/D.8802readings/mcsherrystoc04.pdf

K. Avrachenkov, P. Jacquet, and J. K. Sreedharan, Distributed spectral decomposition in networks by complex diffusion and quantum random walk, IEEE INFOCOM 2016, The 35th Annual IEEE International Conference on Computer Communications, 2016.
DOI : 10.1109/INFOCOM.2016.7524376

URL : https://hal.archives-ouvertes.fr/hal-01263811

M. Franceschelli, A. Gasparri, A. Giua, and C. Seatzu, Decentralized estimation of Laplacian eigenvalues in multi-agent systems, Automatica, vol.49, issue.4, pp.1031-1036, 2013.
DOI : 10.1016/j.automatica.2013.01.029

URL : https://hal.archives-ouvertes.fr/hal-01479646

T. Sahai, A. Speranzon, and A. Banaszuk, Hearing the clusters of a graph: A distributed algorithm, Automatica, vol.48, issue.1, pp.15-24, 2012.
DOI : 10.1016/j.automatica.2011.09.019

S. Blanes, F. Casas, and A. Murua, Symplectic splitting operator methods for the time-dependent Schr??dinger equation, The Journal of Chemical Physics, vol.124, issue.23, p.234105, 2006.
DOI : 10.1016/S0168-9274(97)00064-0

L. Verlet, Computer "Experiments" on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules, Physical Review, vol.30, issue.1, p.98, 1967.
DOI : 10.1016/0031-8914(64)90224-1

B. Leimkuhler and S. Reich, Simulating Hamiltonian Dynamics, 2004.
DOI : 10.1017/CBO9780511614118

A. Iserles, A First Course in the Numerical Analysis of Differential Equations, 2008.

S. K. Gray and D. E. Manolopoulos, Symplectic integrators tailored to the time???dependent Schr??dinger equation, The Journal of Chemical Physics, vol.104, issue.18, pp.7099-7112, 1996.
DOI : 10.1063/1.431514

M. E. Newman, Finding community structure in networks using the eigenvectors of matrices, Physical Review E, vol.49, issue.3, p.36104, 2006.
DOI : 10.1103/PhysRevE.72.046105

K. Avrachenkov, M. Chamie, and G. Neglia, A local average consensus algorithm for wireless sensor networks, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS), pp.1-6, 2011.
DOI : 10.1109/DCOSS.2011.5982199

URL : https://hal.archives-ouvertes.fr/hal-00648185

L. Xiao and S. Boyd, Fast linear iterations for distributed averaging, Systems & Control Letters, vol.53, issue.1, pp.65-78, 2004.
DOI : 10.1016/j.sysconle.2004.02.022

M. Chamie, G. Neglia, and K. Avrachenkov, Reducing communication overhead for average consensus, IEEE IFIP Networking, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00720687

D. Meng and Y. Jia, Iterative learning approaches to design finite-time consensus protocols for multi-agent systems, Systems & Control Letters, vol.61, issue.1, pp.187-194, 2012.
DOI : 10.1016/j.sysconle.2011.10.013

R. W. Longman and Y. Huang, The Phenomenon of Apparent Convergence Followed by Divergence in Learning and Repetitive Control, Intelligent Automation & Soft Computing, vol.8, issue.2, pp.107-128, 2002.
DOI : 10.1007/978-1-4615-5629-9_7