R. Babbar, I. Partalas, C. Metzig, E. Gaussier, and M. Amini, Comparative Classifier Evaluation for Web-Scale Taxonomies Using Power Law, European Semantic Web Conference, 2013.
DOI : 10.1007/978-3-642-41242-4_56

URL : https://hal.archives-ouvertes.fr/hal-00944212

A. Barabási and R. Albert, Emergence of scaling in random networks, science, vol.286, issue.5439, pp.509-512, 1999.

S. Bengio, J. Weston, and D. Grangier, Label embedding trees for large multi-class tasks, Neural Information Processing Systems, pp.163-171, 2010.

P. N. Bennett and N. Nguyen, Refined experts, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pp.11-18, 2009.
DOI : 10.1145/1571941.1571946

L. Bottou and O. Bousquet, The tradeoffs of large scale learning, Advances In Neural Information Processing Systems, pp.161-168, 2008.

L. Cai and T. Hofmann, Hierarchical document categorization with support vector machines, Proceedings of the Thirteenth ACM conference on Information and knowledge management , CIKM '04, pp.78-87, 2004.
DOI : 10.1145/1031171.1031186

A. Capocci, V. D. Servedio, F. Colaiori, L. S. Buriol, D. Donato et al., Preferential attachment in the growth of social networks: The internet encyclopedia Wikipedia, Physical Review E, vol.74, issue.3, p.74036116, 2006.
DOI : 10.1103/PhysRevE.74.036116

O. Dekel, J. Keshet, and Y. Singer, Large margin hierarchical classification, Twenty-first international conference on Machine learning , ICML '04, pp.27-34, 2004.
DOI : 10.1145/1015330.1015374

S. N. Dorogovtsev and J. F. Mendes, Evolution of networks with aging of sites, Physical Review E, vol.62, issue.2, p.1842, 2000.
DOI : 10.1103/PhysRevE.62.1842

L. Egghe, Untangling Herdan's law and Heaps' law: Mathematical and informetric arguments, Journal of the American Society for Information Science and Technology, vol.25, issue.5, pp.702-709, 2007.
DOI : 10.1002/asi.20524

M. Faloutsos, P. Faloutsos, and C. Faloutsos, On power-law relationships of the internet topology

R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, LIBLIN- EAR: A library for large linear classification, Journal of Machine Learning Research, vol.9, pp.1871-1874, 2008.

T. Gao and D. Koller, Discriminative learning of relaxed hierarchy for largescale visual recognition, IEEE International Conference on Computer Vision (ICCV), pp.2072-2079, 2011.

M. M. Geipel, C. J. Tessone, and F. Schweitzer, A complementary view on the growth of directory trees, The European Physical Journal B, vol.35, issue.4, pp.641-648, 2009.
DOI : 10.1140/epjb/e2009-00302-5

S. Gopal, Y. Yang, B. Bai, and A. Niculescu, Bayesian models for large-scale hierarchical classification, Neural Information Processing Systems, 2012.

K. Klemm, V. M. Eguíluz, and M. , Scaling in the structure of directory trees in a computer cluster. Physical review letters, p.95128701, 2005.

D. Koller and M. Sahami, Hierarchically classifying documents using very few words, Proceedings of the Fourteenth International Conference on Machine Learning, ICML '97, 1997.

T. Liu, Y. Yang, H. Wan, H. Zeng, Z. Chen et al., Support vector machines classification with a very large-scale taxonomy, ACM SIGKDD Explorations Newsletter, vol.7, issue.1, 2005.
DOI : 10.1145/1089815.1089821

B. Mandelbrot, A note on a class of skew distribution functions: Analysis and critique of a paper by H. A. Simon, Information and Control, vol.2, issue.1, pp.90-99, 1959.
DOI : 10.1016/S0019-9958(59)90098-1

C. Metzig and M. B. Gordon, A model for scaling in firms??? size and growth rate distribution, Physica A: Statistical Mechanics and its Applications, vol.398, 2014.
DOI : 10.1016/j.physa.2013.11.027

URL : https://hal.archives-ouvertes.fr/hal-00909771

M. Newman, Power laws, pareto distributions and zipf's law. Contemporary Physics, pp.323-351, 2005.

M. E. Newman, Power laws, Pareto distributions and Zipf's law. Contemporary Physics, 2005.
DOI : 10.1080/00107510500052444

URL : http://arxiv.org/abs/cond-mat/0412004

I. Partalas, R. Babbar, ´. E. Gaussier, and C. Amblard, Adaptive Classifier Selection in Large-Scale Hierarchical Classification, ICONIP, pp.612-619, 2012.
DOI : 10.1007/978-3-642-34487-9_74

URL : https://hal.archives-ouvertes.fr/hal-00750771

P. Richmond and S. Solomon, POWER LAWS ARE DISGUISED BOLTZMANN LAWS, International Journal of Modern Physics C, vol.12, issue.03, pp.333-343, 2001.
DOI : 10.1142/S0129183101001754

H. A. Simon, On a class of skew distribution functions, Biometrika, vol.424, issue.3, pp.425-440, 1955.

C. Song, S. Havlin, and H. A. Makse, Self-similarity of complex networks, Nature, vol.28, issue.7024, pp.392-395, 2005.
DOI : 10.1038/35036627

H. Takayasu, A. Sato, and M. Takayasu, Stable Infinite Variance Fluctuations in Randomly Amplified Langevin Systems, Physical Review Letters, vol.79, issue.6, pp.966-969, 1997.
DOI : 10.1103/PhysRevLett.79.966

C. J. Tessone, M. M. Geipel, and F. Schweitzer, Sustainable growth in complex networks, EPL (Europhysics Letters), vol.96, issue.5, p.58005, 2011.
DOI : 10.1209/0295-5075/96/58005

K. G. Wilson and J. Kogut, The renormalization group and the ?? expansion, Physics Reports, vol.12, issue.2, pp.75-199, 1974.
DOI : 10.1016/0370-1573(74)90023-4

G. Xue, D. Xing, Q. Yang, and Y. Yu, Deep classification in largescale text hierarchies, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pp.619-626, 2008.

Y. Yang, J. Zhang, and B. Kisiel, A scalability analysis of classifiers in text categorization, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval , SIGIR '03, pp.96-103, 2003.
DOI : 10.1145/860435.860455

G. U. Yule, A mathematical theory of evolution, based on the conclusions of dr. jc willis, frs, Philosophical Transactions of the Royal Society of London. Series B, Containing Papers of a Biological Character, pp.21-87, 1925.