R. Albert and A. Barabási, Statistical mechanics of complex networks, Reviews of Modern Physics, vol.86, issue.1, pp.47-97, 2002.
DOI : 10.1103/PhysRevLett.86.5835

R. Albert, H. Jeong, and A. Barabási, Diameter of the World-Wide Web, Nature, vol.82, issue.6749, pp.130-131, 1999.
DOI : 10.1103/PhysRevLett.82.3180

D. Arthur and S. Vassilvitskii, k-means++: The advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp.1027-1035, 2007.

A. Bagga and B. Baldwin, Algorithms for scoring coreference chains, The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference, pp.563-566, 1998.

S. Basu, A. Banerjee, and R. J. Mooney, Semi-supervised clustering by seeding, Proceedings of the Nineteenth International Conference on Machine Learning, ICML '02, pp.27-34, 2002.

S. Basu, M. Bilenko, and R. J. Mooney, A probabilistic framework for semi-supervised clustering, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.59-68, 2004.
DOI : 10.1145/1014052.1014062

M. Belkin and P. Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Neural Computation, vol.15, issue.6, pp.1373-1396, 2002.
DOI : 10.1126/science.290.5500.2319

URL : http://jupiter.math.nctu.edu.tw/%7Eweng/courses/2010_topic_discrete/Spectrum/Laplacian.pdf

M. Belkin, P. Niyogi, and V. Sindhwani, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, The Journal of Machine Learning Research, vol.7, pp.2399-2434, 2006.

Y. Bengio, O. Delalleau, and N. L. Roux, Label propagation and quadratic criterion, Semi-Supervised Learning, pp.193-216, 2006.

E. Bengtson and D. Roth, Understanding the value of features for coreference resolution, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.294-303, 2008.
DOI : 10.3115/1613715.1613756

P. Stephen, L. Boyd, and . Vandenberghe, Convex optimization, 2004.

J. Cai, Coreference Resolution via Hypergraph Partitioning, 2012.

D. Chatel, P. Denis, and M. Tommasi, Fast Gaussian Pairwise Constrained Spectral Clustering, Machine Learning and Knowledge Discovery in Databases, pp.242-257, 2014.
DOI : 10.1007/978-3-662-44848-9_16

URL : https://hal.archives-ouvertes.fr/hal-01017269

F. R. Chung, Spectral Graph Theory, 1997.
DOI : 10.1090/cbms/092

A. K. Raymond, J. M. Cox, K. H. Felton, and . Chung, The concentration of commercial success in popular music: An analysis of the distribution of gold records, Journal of Cultural Economics, vol.19, issue.4, pp.333-340, 1995.

E. Mark, A. Crovella, and . Bestavros, Self-similarity in world wide web traffic: Evidence and possible causes, IEEE/ACM Trans. Netw, vol.5, issue.6, pp.835-846, 1997.

M. Cucuringu, I. Koutis, and S. Chawla, Scalable constrained clustering: A generalized spectral method, 2015.

I. Davidson and S. S. Ravi, -Means Algorithm, Proc. 5th SIAM Data Mining Conference, 2005.
DOI : 10.1137/1.9781611972757.13

D. J. De-solla and . Price, Networks of Scientific Papers, Science, vol.149, issue.3683, pp.510-515, 1965.
DOI : 10.1126/science.149.3683.510

M. D. Gianna and . Corso, Estimating an eigenvector by the power method with a random start, SIAM Journal on Matrix Analysis and Applications, vol.18, issue.4, pp.913-937, 1997.

I. Dhillon, Y. Guan, and B. Kulis, A unified view of kernel k-means, spectral clustering and graph cuts. Citeseer, 2004.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, 2001.

D. Easley and J. Kleinberg, Networks, crowds, and markets: reasoning about a highly connected world, 2010.
DOI : 10.1017/CBO9780511761942

P. Erdos and A. Renyi, On the evolution of random graphs, Publ. Math. Inst. Hungary. Acad. Sci, vol.5, pp.17-61, 1960.

L. Ertöz, M. Steinbach, and V. Kumar, Finding Topics in Collections of Documents: A Shared Nearest Neighbor Approach, Clustering and Information Retrieval, vol.11, pp.83-103, 2003.
DOI : 10.1007/978-1-4613-0227-8_3

X. Fan, Y. Zeng, and L. Cao, Non-parametric power-law data clustering . arXiv preprint, 2013.

A. L. Fradkov and V. A. Yakubovich, The s-procedure and the duality relation in convex quadratic programming problems, 1973.

B. Gutenberg and C. F. Richter, Frequency of earthquakes in california, pp.185-188, 1944.

L. Hagen, S. Member, and A. B. Kahng, New spectral methods for ratio cut partition and clustering, IEEE Trans. on Computer-Aided Design, pp.1074-1085, 1992.
DOI : 10.1109/43.159993

M. Hein and S. Setzer, Beyond spectral clustering -tight relaxations of balanced graph cuts, Advances in Neural Information Processing Systems (NIPS, 2011.

A. Bernardo, L. A. Huberman, and . Adamic, The nature of markets in the world wide web. Computing in Economics and Finance, Society for Computational Economics, vol.521, 1999.

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/978-3-642-69024-2_27

H. Ishwaran, F. Lancelot, and . James, Gibbs Sampling Methods for Stick-Breaking Priors, Journal of the American Statistical Association, vol.96, issue.453, pp.161-173, 2001.
DOI : 10.1198/016214501750332758

URL : http://www.bio.ri.ccf.org/Academic/ishwaran/stickBreaking.ps

J. H. Jones and M. S. Handcock, An assessment of preferential attachment as a mechanism for human sexual network formation, Proceedings of the Royal Society B: Biological Sciences, vol.270, issue.1520, pp.1123-1128, 1520.
DOI : 10.1098/rspb.2003.2369

T. Ulf and . Jönsson, A lecture on the s-procedure, 2001.

D. Sepandar, D. Kamvar, C. D. Klein, and . Manning, Spectral learning, IJCAI, pp.561-566, 2003.

H. W. Kuhn, The Hungarian method for the assignment problem, Naval Research Logistics Quarterly, vol.3, issue.1-2, pp.83-97, 1955.
DOI : 10.2140/pjm.1953.3.369

B. Kulis, S. Basu, I. Dhillon, and R. J. Mooney, Semi-supervised graph clustering, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.457-464, 2005.
DOI : 10.1145/1102351.1102409

E. Lassalle and P. Denis, Joint anaphoricity detection and coreference resolution with constrained latent structures, Proceedings of the Twenty- Ninth AAAI Conference on Artificial Intelligence, pp.2274-2280, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01205189

H. Lee, Y. Peirsman, A. Chang, N. Chambers, M. Surdeanu et al., Stanford's multi-pass sieve coreference resolution system at the conll-2011 shared task, Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, pp.28-34, 2011.

J. R. Lee, L. Shayan-oveis-gharan, and . Trevisan, Multi-way spectral partitioning and higher-order cheeger inequalities, Proceedings of the 44th symposium on Theory of Computing, STOC '12, pp.1117-1130, 2012.
DOI : 10.1145/2213977.2214078

Y. Li and L. Chen, Big Biological Data: Challenges and Opportunities, Genomics, Proteomics & Bioinformatics, vol.12, issue.5, pp.187-189, 2014.
DOI : 10.1016/j.gpb.2014.10.001

URL : https://doi.org/10.1016/j.gpb.2014.10.001

Z. Li and J. Liu, Constrained clustering by spectral kernel learning, Computer vision IEEE 12th international conference on, pp.421-427, 2009.

Z. Li, J. Liu, and X. Tang, Constrained clustering via spectral regularization, Computer Vision and Pattern Recognition CVPR 2009. IEEE Conference on, pp.421-428, 2009.

W. Li, Zipf's law everywhere, Glottometrics, vol.5, pp.14-21, 2002.

A. J. Lotka, The frequency distribution of scientific production, Journal of Washingtonian Academy of Science, vol.16, pp.317-323, 1926.

Z. Lu and M. A. Carreira-perpinán, Constrained spectral clustering through affinity propagation, Computer Vision and Pattern Recognition CVPR 2008. IEEE Conference on, pp.1-8, 2008.

E. T. Lu and R. J. Hamilton, Avalanches and the distribution of solar flares, The Astrophysical Journal, vol.380, pp.89-92, 1991.
DOI : 10.1086/186180

X. Luo, On coreference resolution performance metrics, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing , HLT '05, pp.25-32, 2005.
DOI : 10.3115/1220575.1220579

URL : http://acl.ldc.upenn.edu/H/H05/H05-1004.pdf

A. I. Lur-'e and V. N. Postnikov, On the theory of stability of control systems Applied mathematics and mechanics, 1944.

L. Ulrike-von, A tutorial on spectral clustering. CoRR, abs/0711.0189, 2007.

S. Mannor, I. Menache, A. Hoze, and U. Klein, Dynamic abstraction in reinforcement learning via clustering, Twenty-first international conference on Machine learning , ICML '04, p.71, 2004.
DOI : 10.1145/1015330.1015355

URL : http://www.aicml.cs.ualberta.ca/banff04/icml/pages/papers/104.pdf

D. Mavroeidis, Accelerating spectral clustering with partial supervision, Data Mining and Knowledge Discovery, vol.17, issue.4, pp.241-258, 2010.
DOI : 10.1007/s10618-010-0191-9

D. Mavroeidis, Mind the eigen-gap, or how to accelerate semi-supervised spectral learning algorithms, IJCAI Proceedings-International Joint Conference on Artificial Intelligence, p.2692, 2011.

M. Meil?, Comparing clusterings???an information based distance, Journal of Multivariate Analysis, vol.98, issue.5, pp.873-895, 2007.
DOI : 10.1016/j.jmva.2006.11.013

L. Miculicich-werlen and A. Popescu-belis, Using coreference links to improve spanish-to-english machine translation. Idiap-RR Idiap, 2017.

B. Mohar, Y. Alavi, G. Chartrand, and O. R. Oellermann, The laplacian spectrum of graphs, Graph theory, combinatorics, and applications, pp.871-898, 1991.

B. Mohar, Some applications of Laplace eigenvalues of graphs, 1997.
DOI : 10.1007/978-94-015-8937-6_6

G. Neukum and B. A. Ivanov, Crater size distributions and impact probabilities on earth from lunar, terrestrial-planet, and asteroid cratering data, Hazards Due to Comets and Asteroids, p.359, 1994.

M. E. Newman and J. Park, Why social networks are different from other types of networks, Physical Review E, vol.66, issue.3, p.36122, 2003.
DOI : 10.1103/PhysRevE.66.036126

M. E. Newman, The Structure and Function of Complex Networks, SIAM Review, vol.45, issue.2, pp.167-256, 2003.
DOI : 10.1137/S003614450342480

E. Mark and . Newman, Power laws, pareto distributions and zipf's law. Contemporary physics, pp.323-351, 2005.

V. Ng and C. Cardie, Improving machine learning approaches to coreference resolution. page 104, 2001.
DOI : 10.3115/1073083.1073102

Y. Andrew, M. I. Ng, Y. Jordan, and . Weiss, On spectral clustering: Analysis and an algorithm, In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS, pp.849-856, 2001.

K. Malay and . Pakhira, A modified k-means algorithm to avoid empty clusters, International Journal of Recent Trends in Engineering, vol.1, issue.1, 2009.

R. Peng, H. Sun, and L. Zanetti, Partitioning Well-Clustered Graphs: Spectral Clustering Works!, Proceedings of The 28th Conference on Learning Theory, pp.1423-1455, 2015.
DOI : 10.1137/15M1047209

M. Perman, J. Pitman, and M. Yor, Size-biased sampling of poisson point processes and excursions. Probability Theory and Related Fields, pp.21-39, 1992.
DOI : 10.1007/bf01205234

. Mihael and . Perman, Random discrete distributions derived from subordinators : dissertation . [M. Perman], 1990.

J. Pitman, Combinatorial stochastic processes, 2002.

J. Pitman and M. Yor, The two-parameter poisson-dirichlet distribution derived from a stable subordinator. The Annals of Probability, pp.855-900, 1997.
DOI : 10.1214/aop/1024404422

URL : ftp://stat-ftp.Berkeley.EDU/pub/users/pitman/pd2.ps.Z

A. Michael, . Poole, N. Patrick, and . Farrell, The assumptions of the linear regression model. Transactions of the Institute of British Geographers, pp.145-158, 1971.

S. Pradhan, A. Moschitti, N. Xue, O. Uryupina, and Y. Zhang, Conll-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes, Joint Conference on EMNLP and CoNLL -Shared Task, CoNLL '12, pp.1-40

S. Syama, M. Rangapuram, and . Hein, Constrained 1-spectral clustering, International Conference on Artificial Intelligence and Statistics, pp.1143-1151, 2012.

C. David, . Roberts, L. Donald, and . Turcotte, Fractality and self-organized criticality of wars, Fractals, vol.6, issue.04, pp.351-357, 1998.

R. Sah and R. Kohli, Market shares: Some power law results and observations . Working Papers 0401, 2004.

J. Shi and J. Malik, Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.22, issue.8, pp.888-905, 2000.

J. Steinberger, M. Kabadjov, and M. Poesio, Coreference Applications to Summarization, Anaphora Resolution: Algorithms, Resources, and Applications, pp.433-456
DOI : 10.1007/978-3-642-15998-5_7

M. Stoer and F. Wagner, A simple min-cut algorithm, Journal of the ACM, vol.44, issue.4, pp.585-591, 1997.
DOI : 10.1145/263867.263872

M. Vilain, J. Burger, J. Aberdeen, D. Connolly, and L. Hirschman, A model-theoretic coreference scoring scheme, Proceedings of the 6th conference on Message understanding , MUC6 '95, p.45, 1995.
DOI : 10.3115/1072399.1072405

U. Von-luxburg, A. Radl, and M. Hein, Getting lost in space: Large sample analysis of the commute distance, Advances in Neural Information Processing Systems, pp.2622-2630, 2010.

D. Wagner and F. Wagner, Between Min Cut and Graph Bisection, Proceedings of the 18th International Symposium on Mathematical Foundations of Computer Science, MFCS '93, pp.744-750, 1993.
DOI : 10.1007/3-540-57182-5_65

URL : ftp://ftp.math.tu-berlin.de/pub/Preprints/combi/Report-307-1991.ps.Z

S. Warshall, A Theorem on Boolean Matrices, Journal of the ACM, vol.9, issue.1, pp.11-12, 1962.
DOI : 10.1145/321105.321107

J. C. Willis and G. U. Yule, Some Statistics of Evolution and Geographical Distribution in Plants and Animals and their Significance, Nature, vol.109, issue.2728, pp.177-179, 1922.
DOI : 10.1038/109177a0

V. A. Yakubovich, S-procedure in nonlinear control theory, 1971.

V. A. Yakubovich, Minimization of quadratic functionals under quadratic constraints and the necessity of a frequency condition in the quadratic criterion for absolute stability of nonlinear control systems, Soviet Mathematics Doklady, pp.593-596, 1973.

L. Yen, F. Fouss, C. Decaestecker, P. Francq, and M. Saerens, Graph Nodes Clustering Based on the Commute-Time Kernel, Advances in Knowledge Discovery and Data Mining, pp.1037-1045, 2007.
DOI : 10.1007/978-3-540-71701-0_117

H. Damián, . Zanette, C. Susanna, and . Manrubia, Vertical transmission of culture and the distribution of family names. Physica A: Statistical Mechanics and its Applications, pp.1-8, 2001.

D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, Learning with local and global consistency, Advances in Neural Information Processing Systems 16, pp.321-328, 2004.

X. Zhou, J. Zhang, and B. Kulis, Power-law graph cuts. arXiv preprint arXiv:1411, 1971.

X. Zhu, Semi-supervised learning with graphs, 2005.

G. Kingsley and Z. , Human behavior and the principle of least effort: an introduction to human ecology, 2012.