E. Airoldi, D. Blei, S. Fienberg, and E. Xing, Mixed membership stochastic blockmodels, The Journal of Machine Learning Research, vol.9, pp.1981-2014, 2008.

R. Albert and A. Barabási, Statistical mechanics of complex networks, Reviews of Modern Physics, vol.74, issue.1, pp.47-97, 2002.
DOI : 10.1103/RevModPhys.74.47

L. Amaral, A. Scala, M. Barthélémy, and H. Stanley, Classes of small-world networks, Proceedings of the National Academy of Sciences, pp.11149-11152, 2000.
DOI : 10.1073/pnas.200327197

N. Aronszajn, Theory of reproducing kernels. Transactions of the, pp.337-404, 1950.

M. Aupetit and T. Catz, High-dimensional labeled data analysis with topology representing graphs, Neurocomputing, vol.63, pp.139-169, 2005.
DOI : 10.1016/j.neucom.2004.04.009

A. Barabasi and R. Albert, Emergence of scaling in random networks, Science, vol.286, pp.509-512, 1999.

A. Barabási and Z. Oltvai, Network biology: understanding the cell's functional organization, Nature Reviews Genetics, vol.5, issue.2, pp.101-113, 2004.
DOI : 10.1038/nrg1272

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.

P. Bickel and A. Chen, A nonparametric view of network models and Newman???Girvan and other modularities, Proceedings of the National Academy of Sciences, pp.21068-21073, 2009.
DOI : 10.1073/pnas.0907096106

V. D. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol.2008, issue.10, p.10008, 2008.
DOI : 10.1088/1742-5468/2008/10/P10008
URL : https://hal.archives-ouvertes.fr/hal-01146070

. Kriegel, Protein function prediction via graph kernels, Bioinformatics, vol.21, issue.1, pp.47-56, 2005.

U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer et al., On modularity clustering. Knowledge and Data Engineering, IEEE Transactions on, vol.20, issue.2, pp.172-188, 2008.
DOI : 10.1109/tkde.2007.190689
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.6623

H. Bunke, Error correcting graph matching: on the influence of the underlying cost function. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.21, issue.9, pp.917-922, 1999.

H. Bunke, Graph matching: Theoretical foundations, algorithms, and applications, Proceedings of Vision Interface, pp.82-88, 2000.

A. Clauset, C. R. Shalizi, and M. E. Newman, Power-Law Distributions in Empirical Data, SIAM Review, vol.51, issue.4, pp.661-703, 2009.
DOI : 10.1137/070710111

E. Côme and P. Latouche, Model selection and clustering in stochastic block models with the exact integrated complete data likelihood. ArXiv e-prints, 2013.

J. Daudin, F. Picard, and S. Robin, A mixture model for random graphs, Statistics and Computing, vol.4, issue.2, pp.173-183, 2008.
DOI : 10.1007/s11222-007-9046-7
URL : https://hal.archives-ouvertes.fr/inria-00070186

C. Dhanjal, S. Blanchemanche, S. Clémençon, A. Rona-tas, and F. Rossi, Dissemination of health information within social networks, Networks in Social Policy Problems, pp.15-46
URL : https://hal.archives-ouvertes.fr/hal-00753217

S. Dorogovtsev, J. Mendes, and A. Samukhin, Structure of Growing Networks with Preferential Linking, Physical Review Letters, vol.85, issue.21, pp.4633-4636, 2000.
DOI : 10.1103/PhysRevLett.85.4633

C. Dubois, C. Butts, and P. Smyth, Stochastic blockmodelling of relational event dynamics, International Conference on Artificial Intelligence and Statistics, volume 31 of the Journal of Machine Learning Research Proceedings, pp.238-246, 2013.

C. Ducruet, Network diversity and maritime flows, Journal of Transport Geography, vol.30, pp.77-88, 2013.
DOI : 10.1016/j.jtrangeo.2013.03.004
URL : https://hal.archives-ouvertes.fr/halshs-00815731

S. Fortin, The graph isomorphism problem, 1996.

S. Fortunato, Community detection in graphs, Physics Reports, vol.486, issue.3-5, pp.75-174, 2010.
DOI : 10.1016/j.physrep.2009.11.002

X. Gao, B. Xiao, D. Tao, and X. Li, A survey of graph edit distance. Pattern Analysis and applications, pp.113-129, 2010.

L. Garton, C. Haythornthwaite, and B. Wellman, Studying Online Social Networks, Journal of Computer-Mediated Communication, vol.81, issue.2, pp.0-0, 1997.
DOI : 10.1111/j.1083-6101.1997.tb00062.x

M. Girvan and M. Newman, Community structure in social and biological networks, Proceedings of the National Academy of Sciences, p.7821, 2002.
DOI : 10.1073/pnas.122653799

A. Goldenberg, A. Zheng, and S. Fienberg, A survey of statistical network models, 2010.

R. Guigourès, M. Boullé, and F. Rossi, A Triclustering Approach for Time Evolving Graphs, 2012 IEEE 12th International Conference on Data Mining Workshops, pp.115-122, 2012.
DOI : 10.1109/ICDMW.2012.61

M. Hagenbuchner, A. Sperduti, and A. Tsoi, Graph self-organizing maps for cyclic and unbounded graphs, Advances in Machine Learning and Computational Intelligence 16th European Symposium on Artificial Neural Networks 2008 16th European Symposium on Artificial Neural Networks, pp.7-91419, 2008.
DOI : 10.1016/j.neucom.2008.12.021
URL : https://works.bepress.com/mhagenbuchner/15/download/

B. Hammer, A. Hasenfuss, F. Rossi, and M. Strickert, Topographic processing of relational data, Proceedings of the 6th International Workshop on Self-Organizing Maps (WSOM 07), 2007.

B. Hammer and B. J. Jain, Neural methods for non-standard data, Proceedings of XIIth European Symposium on Artificial Neural Networks, pp.281-292, 2004.

B. Hammer, A. Micheli, A. Sperduti, and M. Strickert, A general framework for unsupervised processing of structured data, Neurocomputing, vol.57, pp.3-35, 2004.
DOI : 10.1016/j.neucom.2004.01.008

M. Handcock, A. Raftery, and J. Tantrum, Model-based clustering for social networks, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.6, issue.2, pp.301-354, 2007.
DOI : 10.1111/j.1467-9574.2005.00283.x

C. Heaukulani and Z. Ghahramani, Dynamic probabilistic models for latent feature propagation in social networks, Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp.275-283, 2013.

P. Hoff, A. Raftery, and M. Handcock, Latent Space Approaches to Social Network Analysis, Journal of the American Statistical Association, vol.97, issue.460, pp.1090-1098, 2002.
DOI : 10.1198/016214502388618906
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.6390

J. Hofman and C. Wiggins, Bayesian approach to network modularity. Physical review letters, p.258701, 2008.
DOI : 10.1103/physrevlett.100.258701
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724184

Y. Jacob, L. Denoyer, and P. Gallinari, Classification and annotation in social corpora using multiple relations, Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11, pp.1215-1220, 2011.
DOI : 10.1145/2063576.2063752
URL : https://hal.archives-ouvertes.fr/hal-01286078

Y. Jernite, P. Latouche, C. Bouveyron, P. Rivera, L. Jegou et al., The random subgraph model for the analysis of an acclesiastical network in merovingian gaul, Annals of Applied Statistics, 2013.

C. Kemp, J. Tenenbaum, T. Griffiths, T. Yamada, and N. Ueda, Learning systems of concepts with an infinite relational model, Proceedings of the National Conference on Artificial Intelligence, p.381, 2006.

V. Lacroix, C. Fernandes, and M. Sagot, Motif Search in Graphs: Application to Metabolic Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.3, issue.4, pp.360-368, 2006.
DOI : 10.1109/TCBB.2006.55
URL : https://hal.archives-ouvertes.fr/hal-00427984

P. Latouche, E. Birmelé, and C. Ambroise, Advances in Data Analysis Data Handling and Business Intelligence, chapter Bayesian methods for graph clustering, pp.229-239, 2009.

P. Latouche, E. Birmelé, and C. Ambroise, Overlapping stochastic block models with application to the French political blogosphere, The Annals of Applied Statistics, vol.5, issue.1, pp.309-336, 2011.
DOI : 10.1214/10-AOAS382SUPP
URL : https://hal.archives-ouvertes.fr/hal-00624538

P. Latouche, E. Birmelé, and C. Ambroise, Variational Bayesian inference and complexity control for stochastic block models, Statistical Modelling, vol.12, issue.1, pp.93-115, 2012.
DOI : 10.1177/1471082X1001200105
URL : https://hal.archives-ouvertes.fr/hal-00624536

P. Latouche, E. Birmelé, and C. Ambroise, The Handbook on Mixed Membership Models and Their Applications, chapter Overlapping clustering methods for networks, pp.547-567, 2014.

V. I. Levenshtein, Binary codes capable of correcting deletions, insertions and reversals, Sov. Phys. Dokl, vol.6, pp.707-710, 1966.

E. M. Luks, Isomorphism of graphs of bounded valence can be tested in polynomial time, Journal of Computer and System Sciences, vol.25, issue.1, pp.42-65, 1982.
DOI : 10.1016/0022-0000(82)90009-5

M. Mariadassou, S. Robin, and C. Vacher, Uncovering latent structure in valued graphs: A variational approach, The Annals of Applied Statistics, vol.4, issue.2, pp.715-742, 2010.
DOI : 10.1214/07-AOAS361SUPP
URL : https://hal.archives-ouvertes.fr/hal-01197514

C. Matias and S. Robin, Modeling heterogeneity in random graphs through latent space models: a selective review, Esaim Proc. and Surveys, pp.55-74, 2014.
DOI : 10.1051/proc/201447004
URL : https://hal.archives-ouvertes.fr/hal-00948421

A. Mc-daid, T. Murphy, F. N. , and N. Hurley, Improved Bayesian inference for the stochastic block model with application to large networks, Computational Statistics & Data Analysis, vol.60, pp.12-31, 2013.
DOI : 10.1016/j.csda.2012.10.021

J. Moreno, Who shall survive?: A new approach to the problem of human interrelations. Nervous and Mental, 1934.

M. Newman, Fast algorithm for detecting community structure in networks, Physical Review E, vol.69, issue.6, 2004.
DOI : 10.1103/PhysRevE.69.066133

M. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical Review E, vol.69, issue.2, p.26113, 2004.
DOI : 10.1103/PhysRevE.69.026113

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

A. Noack and R. Rotta, Multi-level Algorithms for Modularity Clustering, SEA '09: Proceedings of the 8th International Symposium on Experimental Algorithms, pp.257-268, 2009.
DOI : 10.1007/978-3-642-02011-7_24

K. Nowicki and T. Snijders, Estimation and Prediction for Stochastic Blockstructures, Journal of the American Statistical Association, vol.96, issue.455, pp.1077-1087, 2001.
DOI : 10.1198/016214501753208735

L. Ralaivola, S. J. Swamidass, H. Saigo, and P. Baldi, Graph kernels for chemical informatics, Neural Networks and Kernel Methods for Structured Domains, pp.1093-1110, 2005.
DOI : 10.1016/j.neunet.2005.07.009

M. G. Rodriguez, J. Leskovec, D. Balduzzi, and B. Schölkopf, Uncovering the structure and temporal dynamics of information propagation, Network Science, vol.5, issue.01, pp.26-65, 2014.
DOI : 10.1137/070704277

F. Rossi, N. Villa-vialaneix, and F. Hautefeuille, Exploration of a large database of French notarial acts with social network methods, Digital Medievalist, vol.9, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01053673

D. H. Rouvray and A. T. Balaban, Chemical applications of graph theory, Applications of Graph Theory, pp.177-221, 1979.

M. Salter-townshend and T. B. Murphy, Variational Bayesian inference for the Latent Position Cluster Model for network data, Computational Statistics & Data Analysis, vol.57, issue.1, pp.661-671, 2013.
DOI : 10.1016/j.csda.2012.08.004

M. Salter-townshend, A. White, I. Gollini, and T. B. Murphy, Review of statistical network analysis: models, algorithms, and software, Statistical Analysis and Data Mining, vol.4, issue.4, pp.243-264, 2012.
DOI : 10.1002/sam.11146

S. E. Schaeffer, Graph clustering, Computer Science Review, vol.1, issue.1, pp.27-64, 2007.
DOI : 10.1016/j.cosrev.2007.05.001

B. Schölkopf and A. Smola, Learning with Kernels, 2002.

J. Shawe-taylor and N. Cristianini, Kernel Methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

T. Snijders and K. Nowicki, Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure, Journal of Classification, vol.14, issue.1, pp.75-100, 1997.
DOI : 10.1007/s003579900004

S. Strogatz, Exploring complex networks, Nature, vol.84, issue.6825, pp.268-276, 2001.
DOI : 10.1038/35065725

J. B. Tenenbaum, V. De-silva, and J. C. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000.
DOI : 10.1126/science.290.5500.2319

S. V. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt, Graph kernels, The Journal of Machine Learning Research, vol.11, pp.1201-1242, 2010.

U. and V. Luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1007/s11222-007-9033-z

D. Watts and S. Strogatz, Collective dynamics of small-world networks, Nature, vol.393, issue.6684, pp.440-442, 1998.
DOI : 10.1038/30918

E. Xing, W. Fu, and L. Song, A state-space mixed membership blockmodel for dynamic network tomography, The Annals of Applied Statistics, vol.4, issue.2, pp.535-566, 2010.
DOI : 10.1214/09-AOAS311

K. S. Xu, A. O. Hero, and I. , Dynamic stochastic blockmodels: Statistical models for timeevolving networks, Social Computing, Behavioral-Cultural Modeling and Prediction, pp.201-210, 2013.

T. Yang, Y. Chi, S. Zhu, Y. Gong, and R. Jin, Detecting communities and their evolutions in dynamic social networks???a??Bayesian approach, Machine Learning, vol.2, issue.1, pp.157-189, 2011.
DOI : 10.1007/s10994-010-5214-7

H. Zanghi, C. Ambroise, and V. Miele, Fast online graph clustering via Erd??s???R??nyi mixture, Pattern Recognition, vol.41, issue.12, pp.3592-3599, 2008.
DOI : 10.1016/j.patcog.2008.06.019

H. Zanghi, S. Volant, and C. Ambroise, Clustering based on random graph model embedding vertex features, Pattern Recognition Letters, vol.31, issue.9, pp.830-836, 2010.
DOI : 10.1016/j.patrec.2010.01.026