A. E. , Community detection and stochastic block models: recent developments, J Mach Learn Res, vol.18, issue.177, pp.1-86, 2018.

S. Fortunato, Community detection in graphs, Phys Rep, vol.486, issue.3, pp.75-174, 2010.

S. Fortunato and D. Hric, Community detection in networks: a user guide, Phys Rep, vol.659, pp.1-44, 2016.

A. Jonnalagadda and L. Kuppusamy, A survey on game theoretic models for community detection in social networks, Soc Netw Anal Mining, vol.6, issue.1, p.83, 2016.

V. Luxburg and U. , A tutorial on spectral clustering, Stat Comput, vol.17, issue.4, pp.395-416, 2007.

S. E. Schaeffer, Graph clustering, Comput Sci Rev, vol.1, issue.1, pp.27-64, 2007.

K. Avrachenkov, V. Dobrynin, D. Nemirovsky, S. K. Pham, and E. Smirnova, Pagerank based clustering of hypertext document collections, Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, pp.873-877, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00565355

K. Avrachenkov, M. E. Chamie, and G. Neglia, Graph clustering based on mixing time of random walks, IEEE international conference on communications, pp.4089-94, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01087693

S. Dongen, Performance criteria for graph clustering and Markov cluster experiments, Amsterdam: CWI (Centre for Mathematics and Computer Science, 2000.

M. Meil? and J. Shi, A random walks view of spectral segmentation, The 8th international workshop on artifical intelligence and statistics (AISTATS), 2001.

M. E. Newman, A measure of betweenness centrality based on random walks, Soc Netw, vol.27, issue.1, pp.39-54, 2005.
DOI : 10.1016/j.socnet.2004.11.009

URL : http://arxiv.org/pdf/cond-mat/0309045

P. Pons and M. Latapy, Computing communities in large networks using random walks, ISCIS, vol.3733, pp.284-93, 2005.
DOI : 10.1007/11569596_31

URL : http://arxiv.org/pdf/cond-mat/0412368

M. Blatt, S. Wiseman, and E. Domany, Clustering data through an analogy to the potts model, Advances in neural information processing systems 8, NIPS, pp.416-438, 1995.

V. D. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, J Stat Mech, vol.10, p.10008, 2008.
DOI : 10.1088/1742-5468/2008/10/p10008

URL : https://hal.archives-ouvertes.fr/hal-01146070

M. Girvan and M. E. Newman, Community structure in social and biological networks, Proc Natl Acad Sci, vol.99, issue.12, pp.7821-7827, 2002.
DOI : 10.1073/pnas.122653799

URL : http://www.pnas.org/content/99/12/7821.full.pdf

M. E. Newman, Modularity and community structure in networks, Proc Natl Acad Sci, vol.103, issue.23, pp.8577-82, 2006.
DOI : 10.1073/pnas.0601602103

URL : http://www.pnas.org/content/103/23/8577.full.pdf

U. N. Raghavan, R. Albert, and S. Kumara, Near linear time algorithm to detect community structures in large-scale networks, Phys Rev E, vol.76, issue.3, p.36106, 2007.
DOI : 10.1103/physreve.76.036106

URL : http://link.aps.org/pdf/10.1103/PhysRevE.76.036106

J. Reichardt and S. Bornholdt, Statistical mechanics of community detection, Phys Rev E, vol.74, issue.1, p.16110, 2006.
DOI : 10.1103/physreve.74.016110

URL : http://arxiv.org/pdf/cond-mat/0603718

L. Waltman, N. J. Van-eck, and E. C. Noyons, A unified approach to mapping and clustering of bibliometric networks, J Inform, vol.4, issue.4, pp.629-664, 2010.
DOI : 10.1016/j.joi.2010.07.002

URL : https://openaccess.leidenuniv.nl/bitstream/handle/1887/15685/CWTS-WP-2010-008.pdf?sequence=1

P. J. Mcsweeney, K. Mehrotra, and J. C. Oh, A game theoretic framework for community detection, International conference on advances in social networks analysis and mining, ASONAM 2012, pp.227-261, 2012.

V. V. Mazalov and L. I. Trukhina, Generating functions and the myerson vector in communication networks, Discrete Math Appl, vol.24, issue.5, pp.295-303, 2014.
DOI : 10.1515/dma-2014-0026

V. V. Mazalov, K. Avrachenkov, L. Trukhina, and B. T. Tsynguev, Game-theoretic centrality measures for weighted graphs, Fund Inform, vol.145, issue.3, pp.341-58, 2016.
DOI : 10.3233/fi-2016-1364

URL : https://hal.archives-ouvertes.fr/hal-01402858

D. Gomez, E. González-arangüena, C. Manuel, G. Owen, M. Del-pozo et al., Centrality and power in social networks: a game theoretic approach, Math Soc Sci, vol.46, issue.1, pp.27-54, 2003.

N. R. Suri and Y. Narahari, Determining the top-k nodes in social networks using the shapley value, Proceedings of the 7th international joint conference on autonomous agents and multiagent systems. International foundation for autonomous agents and multiagent systems, vol.3, pp.1509-1521

P. L. Szczepa?ski, T. Michalak, and T. Rahwan, A new approach to betweenness centrality based on the shapley value, Proceedings of the 11th international conference on autonomous agents and multiagent systems, vol.1, pp.239-285

T. P. Michalak, K. V. Aadithya, P. L. Szczepanski, B. Ravindran, and N. R. Jennings, Efficient computation of the shapley value for game-theoretic network centrality, J Artif Intell Res, vol.46, pp.607-50, 2013.

W. Chen and S. Teng, Interplay between social influence and network centrality: a comparative study on shapley centrality and single-node-influence centrality, Proceedings of the 26th international conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp.967-973, 2017.
DOI : 10.1145/3038912.3052608

O. Skibski, T. P. Michalak, and T. Rahwan, Axiomatic characterization of game-theoretic centrality, J Artif Intell Res, vol.62, pp.33-68, 2018.

A. Bogomolnaia and M. O. Jackson, The stability of hedonic coalition structures, Games Econ Behav, vol.38, issue.2, pp.201-231, 2002.

S. Fortunato and M. Barthélemy, Resolution limit in community detection, Proc Natl Acad Sci, vol.104, issue.1, pp.36-41, 2007.

J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney, Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters, Internet Math, vol.6, issue.1, pp.29-123, 2009.

L. Hagen and A. B. Kahng, New spectral methods for ratio cut partitioning and clustering, IEEE Trans Comput Aided Design Integ Circuits Syst, vol.11, issue.9, pp.1074-85, 1992.

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans Pattern Anal Mach Intell, vol.22, issue.8, pp.888-905, 2000.

L. Zhou, C. Cheng, K. Lü, and H. Chen, Using coalitional games to detect communities in social networks, International conference on web-age information management, pp.326-357, 2013.

L. Zhou, K. Lü, C. Cheng, and H. Chen, A game theory based approach for community detection in social networks, Proceedings Big Data-29th British national conference on databases, BNCOD 2013, pp.268-81, 2013.

S. Basu and U. Maulik, Community detection based on strong nash stable graph partition, Soci Netw Anal Mining, vol.5, issue.1, p.61, 2015.

K. E. Avrachenkov, A. Y. Kondratev, and V. V. Mazalov, Cooperative game theory approaches for network partitioning, International computing and combinatorics conference (COCOON/CSoNet), pp.591-602, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01560682

R. B. Myerson and . Game-theory, , 2013.

B. Peleg and P. Sudhölter, Introduction to the theory of cooperative games, vol.34, 2007.

V. Mazalov, Mathematical game theory and applications, 2014.

R. B. Myerson, Graphs and cooperation in games, Math Operat Res, vol.2, issue.3, pp.225-234, 1977.

M. O. Jackson, Allocation rules for network games, Games Econ Behav, vol.51, issue.1, pp.128-54, 2005.

M. O. Jackson, Social and economic networks, 2010.

D. A. Levin, Y. Peres, and E. L. Wilmer, Markov chains and mixing times, 2009.

B. Hajek, Cooling schedules for optimal annealing, Math Operat Res, vol.13, issue.2, pp.311-340, 1988.

Q. Berthet, P. Rigollet, and P. Srivastava, Exact recovery in the ising blockmodel, Ann Stat, vol.41, p.1780, 2018.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: machine learning in python, J Mach Learn Res, vol.12, pp.2825-2855, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. Meil? and D. Heckerman, An experimental comparison of model-based clustering methods, Mach Learn, vol.42, issue.1-2, pp.9-29, 2001.

W. W. Zachary, An information flow model for conflict and fission in small groups, J Anthropol Res, vol.33, issue.4, pp.452-73, 1977.

D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten et al., The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations, Behav Ecol Sociobiol, vol.54, issue.4, pp.396-405, 2003.

A. B. Zhizhchenko and A. D. Izaak, The information system Math-Net.Ru. Application of contemporary technologies in the scientific work of mathematicians, Russian Math Surveys, vol.62, issue.5, pp.943-966, 2007.