K. Zhou, A. Martin, Q. Pan, and Z. Liu, Median evidential c-means algorithm and its application to community detection. Knowledge-Based Systems, pp.69-88, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01100902

K. Zhou, A. Martin, and Q. Pan, A similarity-based community detection method with multiple prototype representation. Physica A: Statistical Mechanics and its Applications, pp.519-531, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01185866

K. Zhou, A. Martin, and Q. Pan, The Belief Noisy-OR Model Applied to Network Reliability Analysis, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol.24, issue.06
DOI : 10.1142/S0218488516500434

URL : https://hal.archives-ouvertes.fr/hal-01326342

K. Zhou and A. Martin, Quan Pan, and Zhun-ga Liu. E Evidential Label Propagation Algorithm for Graphs, Conference Papers (1) Information Fusion (FUSION), 2016 19th International Conference on, 2016.

K. Zhou, A. Martin, Q. Pan, and Z. Liu, Evidential relational clustering using medoids, Information Fusion (FUSION), 2015 18th International Conference on, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01176143

K. Zhou, A. Martin, and Q. Pan, Evidential community detection using structural and attribute information, Atelier Réseaux Sociaux et Intelligence Artificielle, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01176326

K. Zhou, A. Martin, and Q. Pan, Evidential Communities for Complex Networks, Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp.557-566, 2014.
DOI : 10.1007/978-3-319-08795-5_57

URL : https://hal.archives-ouvertes.fr/hal-01101172

K. Zhou, A. Martin, and Q. Pan, Evidential-EM Algorithm Applied to Progressively Censored Observations, Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp.180-189, 2014.
DOI : 10.1007/978-3-319-08852-5_19

URL : https://hal.archives-ouvertes.fr/hal-01100840

W. Maalel, K. Zhou, A. Martin, and Z. Elouedi, Belief Hierarchical Clustering, Belief Functions: Theory and Applications, pp.68-76, 2014.
DOI : 10.1109/5326.669565

URL : https://hal.archives-ouvertes.fr/hal-01102028

C. C. Aggarwal and C. K. Reddy, Data clustering: algorithms and applications, 2013.

B. Amiri, L. Hossain, J. W. Crawford, W. , and R. T. , Community detection in complex networks: Multi?objective enhanced firefly algorithm. Knowledge- Based Systems, pp.1-11, 2013.

V. Antoine, B. Quost, M. Masson, and T. Denoeux, CECM: Constrained evidential -means algorithm, Computational Statistics & Data Analysis, vol.56, issue.4, pp.894-914, 2012.
DOI : 10.1016/j.csda.2010.09.021

URL : https://hal.archives-ouvertes.fr/hal-00554310

V. Antoine, B. Quost, M. Masson, and T. Denoeux, CEVCLUS: evidential clustering with instance-level constraints for relational data, Soft Computing, vol.4, issue.5, pp.1321-1335, 2014.
DOI : 10.1007/s00500-013-1146-z

URL : https://hal.archives-ouvertes.fr/hal-00923311

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.

M. Atzmueller, S. Doerfel, and F. Mitzlaff, Description-oriented community detection using exhaustive subgroup discovery, Information Sciences, vol.329, pp.965-984, 2016.
DOI : 10.1016/j.ins.2015.05.008

J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, 1981.
DOI : 10.1007/978-1-4757-0450-1

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, pp.2008-10008, 2008.
DOI : 10.1088/1742-5468/2008/10/P10008

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

C. Borgelt, Prototype-based classification and clustering, 2006.

S. Brin and L. Page, The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, pp.107-117, 1998.

X. Cao, X. Wang, D. Jin, Y. Cao, and D. He, Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization Scientific reports, 2013.

J. Chen, O. R. Za¨?aneza¨?ane, and R. Goebel, Detecting Communities in Social Networks using Max-Min Modularity, SDM, pp.20-24, 2009.
DOI : 10.1137/1.9781611972795.84

Y. Chen, C. Cheng, C. Lai, C. Hsu, and H. Syu, Identifying patients in target customer segments using a two-stage clustering-classification approach: A hospital-based assessment, Computers in biology and medicine, pp.213-221, 2012.
DOI : 10.1016/j.compbiomed.2011.11.010

A. Clauset, M. E. Newman, M. , and C. , Finding community structure in very large networks, Physical Review E, vol.70, issue.6, p.66111, 2004.
DOI : 10.1103/PhysRevE.70.066111

A. Clifton and J. K. Lundquist, Data Clustering Reveals Climate Impacts on Local Wind Phenomena, Journal of Applied Meteorology and Climatology, vol.51, issue.8, pp.511547-1557, 2012.
DOI : 10.1175/JAMC-D-11-0227.1

M. Cottrell, B. Hammer, A. Hasenfuß, and T. Villmann, Batch and median neural gas, Neural Networks, vol.19, issue.6-7, pp.762-771, 2006.
DOI : 10.1016/j.neunet.2006.05.018

URL : https://hal.archives-ouvertes.fr/hal-00107462

A. P. Dempster, Upper and lower probabilities induced by a multivalued mapping . The annals of mathematical statistics, pp.325-339, 1967.

T. Denoeux, A k-nearest neighbor classification rule based on dempster-shafer theory. Systems, Man and Cybernetics, IEEE Transactions on, vol.25, issue.5, pp.804-813, 1995.

T. Denoeux, O. Kanjanatarakul, and S. Sriboonchitta, EK-NNclus: A clustering procedure based on the evidential k-nearest neighbor rule. Knowledge-Based Systems, pp.57-69, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01294267

T. Denoeux and M. Masson, EVCLUS: Evidential Clustering of Proximity Data, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.1, pp.95-109, 2004.
DOI : 10.1109/TSMCB.2002.806496

Y. Dong, Y. Zhuang, K. Chen, T. , and X. , A hierarchical clustering algorithm based on fuzzy graph connectedness. Fuzzy Sets and Systems, pp.1760-1774, 2006.

S. R. Dubey, P. Dixit, N. Singh, and J. P. Gupta, Infected Fruit Part Detection using K-Means Clustering Segmentation Technique, International Journal of Interactive Multimedia and Artificial Intelligence, vol.2, issue.2, pp.65-72, 2013.
DOI : 10.9781/ijimai.2013.229

D. Dubois and H. Prade, Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence, vol.5, issue.1, pp.244-264, 1988.
DOI : 10.1016/0165-0114(78)90029-5

J. Duch and A. Arenas, Community detection in complex networks using extremal optimization, Physical Review E, vol.72, issue.2, p.27104, 2005.
DOI : 10.1103/PhysRevE.72.027104

J. C. Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, vol.3, issue.3, pp.32-57, 1973.
DOI : 10.1080/01969727308546046

E. Eaton and R. Mansbach, A spin-glass model for semi-supervised community detection, AAAI. Citeseer, 2012.

Y. Fan, M. Li, P. Zhang, J. Wu, D. et al., Accuracy and precision of methods for community identification in weighted networks, Physica A: Statistical Mechanics and its Applications, pp.363-372, 2007.
DOI : 10.1016/j.physa.2006.11.036

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

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

S. Fortunato and M. Barthelemy, Resolution limit in community detection, Proceedings of the National Academy of Sciences, pp.36-41, 2007.
DOI : 10.1073/pnas.0605965104

B. Gabrys and A. Bargiela, General fuzzy min-max neural network for clustering and classification, IEEE Transactions on Neural Networks, vol.11, issue.3, pp.769-783, 2000.
DOI : 10.1109/72.846747

C. Gao, D. Wei, Y. Hu, S. Mahadevan, and Y. Deng, A modified evidential methodology of identifying influential nodes in weighted networks, Physica A: Statistical Mechanics and its Applications, pp.3925490-5500, 2013.
DOI : 10.1016/j.physa.2013.06.059

J. Gao, F. Liang, W. Fan, C. Wang, Y. Sun et al., On community outliers and their efficient detection in information networks, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.813-822, 2010.
DOI : 10.1145/1835804.1835907

Y. Gao, H. Qi, D. Liu, J. Li, L. et al., A fuzzy relational clustering algorithm with q-weighted medoids, Journal of Computational Information Systems, vol.10, issue.6, pp.2389-2396, 2014.

T. Geweniger, D. Zülke, B. Hammer, and T. Villmann, Median fuzzy c-means for clustering dissimilarity data, Neurocomputing, vol.73, issue.7-9, pp.731109-1116, 2010.
DOI : 10.1016/j.neucom.2009.11.020

M. Girvan and M. E. Newman, Community structure in social and biological networks, Proceedings of the National Academy of Sciences, pp.7821-7826, 2002.
DOI : 10.1073/pnas.122653799

T. Graepel, R. Herbrich, P. Bollmann-sdorra, and K. Obermayer, Classification on pairwise proximity data Advances in neural information processing systems, pp.438-444, 1999.

D. A. Grossman and O. Frieder, Information retrieval: Algorithms and heuristics, 2012.
DOI : 10.1007/978-1-4615-5539-1

R. Guimera, M. Sales-pardo, and L. A. Amaral, Modularity from fluctuations in random graphs and complex networks, Physical Review E, vol.70, issue.2, p.25101, 2004.
DOI : 10.1103/PhysRevE.70.025101

S. B. Hariz, Z. Elouedi, and K. Mellouli, Clustering approach using belief function theory, Artificial Intelligence: Methodology, Systems, and Applications, pp.162-171, 2006.

R. J. Hathaway and J. C. Bezdek, Nerf c-means: Non-Euclidean relational fuzzy clustering, Pattern Recognition, vol.27, issue.3, pp.429-437, 1994.
DOI : 10.1016/0031-3203(94)90119-8

T. Havens, J. Bezdek, C. Leckie, K. Ramamohanarao, and M. Palaniswami, A soft modularity function for detecting fuzzy communities in social networks. Fuzzy Systems, IEEE Transactions on, vol.21, issue.6, pp.1170-1175, 2013.

T. Hofmann and J. M. Buhmann, Pairwise data clustering by deterministic annealing. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.19, issue.1, pp.1-14, 1997.

Y. Horng, S. Chen, Y. Chang, L. , and C. , A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. Fuzzy Systems, IEEE Transactions on, vol.13, issue.2, pp.216-228, 2005.

Y. Hu, M. Li, P. Zhang, Y. Fan, D. et al., Community detection by signaling on complex networks, Physical Review E, vol.78, issue.1, pp.16115-16116, 2008.
DOI : 10.1103/PhysRevE.78.016115

P. Jaccard, THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1, New Phytologist, vol.11, issue.2, pp.37-50, 1912.
DOI : 10.1111/j.1469-8137.1912.tb05611.x

A. K. Jain, Data clustering: 50 years beyond k-means. Pattern recognition letters, pp.31651-666, 2010.

Y. Jiang, C. Jia, Y. , and J. , An efficient community detection method based on rank centrality, Physica A: Statistical Mechanics and its Applications, 2012.
DOI : 10.1016/j.physa.2012.12.013

L. Kaufman and P. J. Rousseeuw, Finding groups in data: an introduction to cluster analysis, 2009.
DOI : 10.1002/9780470316801

R. Krishnapuram, A. Joshi, O. Nasraoui, Y. , and L. , Low-complexity fuzzy relational clustering algorithms for web mining. Fuzzy Systems, IEEE Transactions on, vol.9, issue.4, pp.595-607, 2001.

R. Krishnapuram and J. M. Keller, A possibilistic approach to clustering. Fuzzy Systems, IEEE Transactions on, vol.1, issue.2, pp.98-110, 1993.

A. Lancichinetti, S. Fortunato, R. , and F. , Benchmark graphs for testing community detection algorithms, Physical Review E, vol.78, issue.4, p.46110, 2008.
DOI : 10.1103/PhysRevE.78.046110

C. Largeron, P. Mougel, R. Rabbany, and O. R. Za¨?aneza¨?ane, Generating Attributed Networks with Communities, PLOS ONE, vol.74, issue.4, p.122777, 2015.
DOI : 10.1371/journal.pone.0122777.t007

URL : https://hal.archives-ouvertes.fr/hal-01223232

M. Li and Z. Zhou, SETRED: Self-training with Editing, Advances in Knowledge Discovery and Data Mining, pp.611-621, 2005.
DOI : 10.1007/11430919_71

M. Lichman, UCI machine learning repository, 2013.

P. Lingras and C. West, Interval Set Clustering of Web Users with Rough K-Means, Journal of Intelligent Information Systems, vol.23, issue.1, pp.5-16, 2004.
DOI : 10.1023/B:JIIS.0000029668.88665.1a

D. Liu, H. Bai, H. Li, W. , and W. , Semi-supervised community detection using label propagation, International Journal of Modern Physics B, vol.28, issue.29, p.281450208, 2014.
DOI : 10.1142/S0217979214502087

J. Liu and T. Liu, Detecting community structure in complex networks using simulated annealing with k-means algorithms. Physica A: Statistical Mechanics and its Applications, pp.3892300-2309, 2010.

W. Liu, Measuring Conflict Between Possibilistic Uncertain Information Through Belief Function Theory, In Knowledge Science, Engineering and Management, pp.265-277, 2006.
DOI : 10.1007/11811220_23

Z. Liu, J. Dezert, G. Mercier, and Q. Pan, Belief C-Means: An extension of Fuzzy C-Means algorithm in belief functions framework, Pattern Recognition Letters, vol.33, issue.3, pp.291-300, 2012.
DOI : 10.1016/j.patrec.2011.10.011

URL : https://hal.archives-ouvertes.fr/hal-01058024

Z. Liu, Q. Pan, J. Dezert, and G. Mercier, Credal c-means clustering method based on belief functions. Knowledge-Based Systems, pp.119-132, 2015.

L. Ma, S. Destercke, W. , and Y. , Online active learning of decision trees with evidential data, Pattern Recognition, vol.52, pp.33-45, 2016.
DOI : 10.1016/j.patcog.2015.10.014

URL : https://hal.archives-ouvertes.fr/hal-01254290

X. Ma, L. Gao, X. Yong, and L. Fu, Semi-supervised clustering algorithm for community structure detection in complex networks, Physica A: Statistical Mechanics and its Applications, pp.187-197, 2010.
DOI : 10.1016/j.physa.2009.09.018

W. Maalel, K. Zhou, A. Martin, and Z. Elouedi, Belief Hierarchical Clustering, Belief Functions: Theory and Applications, pp.68-76, 2014.
DOI : 10.1109/5326.669565

URL : https://hal.archives-ouvertes.fr/hal-01102028

M. Maier, M. Hein, V. Luxburg, and U. , Optimal construction of knearest-neighbor graphs for identifying noisy clusters, Theoretical Computer Science, issue.19, pp.4101749-1764, 2009.

A. Martin and I. Quidu, Decision support with belief functions theory for seabed characterization, Information Fusion 11th International Conference on, pp.1-8, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00281740

M. Masson and T. Denoeux, Clustering interval-valued proximity data using belief functions, Pattern Recognition Letters, vol.25, issue.2, pp.163-171, 2004.
DOI : 10.1016/j.patrec.2003.09.008

M. Masson and T. Denoeux, ECM: An evidential version of the fuzzy c-means algorithm, Pattern Recognition, vol.41, issue.4, pp.1384-1397, 2008.
DOI : 10.1016/j.patcog.2007.08.014

M. Masson and T. Denoeux, RECM: Relational evidential c-means algorithm, Pattern Recognition Letters, vol.30, issue.11, pp.1015-1026, 2009.
DOI : 10.1016/j.patrec.2009.04.008

URL : https://hal.archives-ouvertes.fr/hal-00400273

M. Masson and T. Denoeux, Ensemble clustering in the belief functions framework, International Journal of Approximate Reasoning, vol.52, issue.1, pp.92-109, 2011.
DOI : 10.1016/j.ijar.2010.04.007

URL : https://hal.archives-ouvertes.fr/hal-00651375

J. Mei and L. Chen, Fuzzy clustering with weighted medoids for relational data, Pattern Recognition, vol.43, issue.5, pp.1964-1974, 2010.
DOI : 10.1016/j.patcog.2009.12.007

J. Mei and L. Chen, Fuzzy relational clustering around medoids: A unified view. Fuzzy Sets and Systems, pp.44-56, 2011.

M. Mendes and L. Sacks, Evaluating fuzzy clustering for relevance-based information access, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03., pp.648-653, 2003.
DOI : 10.1109/FUZZ.2003.1209440

S. Mitra, An evolutionary rough partitive clustering, Pattern Recognition Letters, vol.25, issue.12, pp.1439-1449, 2004.
DOI : 10.1016/j.patrec.2004.05.007

S. Mitra, H. Banka, and W. Pedrycz, Rough–Fuzzy Collaborative Clustering, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.36, issue.4, p.795, 2006.
DOI : 10.1109/TSMCB.2005.863371

S. Mitra, W. Pedrycz, and B. Barman, Shadowed c-means: Integrating fuzzy and rough clustering, Pattern Recognition, vol.43, issue.4, pp.1282-1291, 2010.
DOI : 10.1016/j.patcog.2009.09.029

F. Murtagh and P. Contreras, Algorithms for hierarchical clustering: an overview, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.30, issue.1, pp.86-97, 2012.
DOI : 10.1002/widm.53

T. Nepusz, A. Petróczi, L. Négyessy, and F. Bazsó, Fuzzy communities and the concept of bridgeness in complex networks, Physical Review E, vol.77, issue.1, p.16107, 2008.
DOI : 10.1103/PhysRevE.77.016107

M. E. Newman, Fast algorithm for detecting community structure in networks, Physical Review E, vol.69, issue.6, p.66133, 2004.
DOI : 10.1103/PhysRevE.69.066133

M. E. Newman, Finding community structure in networks using the eigenvectors of matrices, Physical Review E, vol.74, issue.3, p.36104, 2006.
DOI : 10.1103/PhysRevE.74.036104

M. E. Newman, Modularity and community structure in networks, Proceedings of the National Academy of Sciences, pp.8577-8582, 2006.
DOI : 10.1073/pnas.0601602103

M. E. 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

N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, A possibilistic fuzzy c-means clustering algorithm. Fuzzy Systems, IEEE Transactions on, vol.13, issue.4, pp.517-530, 2005.

Y. Pan, D. Li, J. Liu, and J. Liang, Detecting community structure in complex networks via node similarity, Physica A: Statistical Mechanics and its Applications, pp.3892849-2857, 2010.
DOI : 10.1016/j.physa.2010.03.006

Z. Pawlak, Rough sets, International Journal of Computer & Information Sciences, vol.8, issue.3, pp.341-356, 1982.
DOI : 10.1007/BF01001956

G. Peters, Some refinements of rough -means clustering, Pattern Recognition, vol.39, issue.8, pp.1481-1491, 2006.
DOI : 10.1016/j.patcog.2006.02.002

U. N. Raghavan, R. Albert, and S. Kumara, Near linear time algorithm to detect community structures in large-scale networks, Physical Review E, vol.76, issue.3, p.76036106, 2007.
DOI : 10.1103/PhysRevE.76.036106

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

M. Rosvall and C. T. Bergstrom, Maps of random walks on complex networks reveal community structure, Proceedings of the National Academy of Sciences, pp.1118-1123, 2008.
DOI : 10.1073/pnas.0706851105

J. W. Sammon, A Nonlinear Mapping for Data Structure Analysis, IEEE Transactions on Computers, vol.18, issue.5, pp.401-409, 1969.
DOI : 10.1109/T-C.1969.222678

J. Schubert, Clustering belief functions based on attracting and conflicting metalevel evidence using Potts spin mean field theory, Information Fusion, vol.5, issue.4, pp.309-318, 2004.
DOI : 10.1016/j.inffus.2003.12.002

J. Scripps, P. Tan, and A. Esfahanian, Exploration of Link Structure and Community-Based Node Roles in Network Analysis, Seventh IEEE International Conference on Data Mining (ICDM 2007), pp.649-654, 2007.
DOI : 10.1109/ICDM.2007.37

B. Settles, Active learning literature survey, pp.55-6611, 2010.

G. Shafer, A mathematical theory of evidence, 1976.

P. Smets, Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem, International Journal of Approximate Reasoning, vol.9, issue.1, pp.1-35, 1993.
DOI : 10.1016/0888-613X(93)90005-X

P. Smets, Decision making in the TBM: the necessity of the pignistic transformation, International Journal of Approximate Reasoning, vol.38, issue.2, pp.133-147, 2005.
DOI : 10.1016/j.ijar.2004.05.003

P. Smets and R. Kennes, The transferable belief model, Artificial Intelligence, vol.66, issue.2, pp.191-234, 1994.
DOI : 10.1016/0004-3702(94)90026-4

URL : https://hal.archives-ouvertes.fr/hal-01185821

K. Subbian, C. C. Aggarwal, J. Srivastava, P. , and S. Y. , Community Detection with Prior Knowledge, SDM, pp.405-413, 2013.
DOI : 10.1137/1.9781611972832.45

F. Wang and C. Zhang, Label propagation through linear neighborhoods. Knowledge and Data Engineering, IEEE Transactions on, vol.20, issue.1, pp.55-67, 2008.

S. White and P. Smyth, A Spectral Clustering Approach To Finding Communities in Graphs, SDM, pp.76-84, 2005.
DOI : 10.1137/1.9781611972757.25

G. Xu, S. Tsoka, and L. G. Papageorgiou, Finding community structures in complex networks using mixed integer optimisation, The European Physical Journal B, vol.579, issue.2, pp.231-239, 2007.
DOI : 10.1140/epjb/e2007-00331-0

W. W. Zachary, An Information Flow Model for Conflict and Fission in Small Groups, Journal of Anthropological Research, vol.33, issue.4, pp.452-473, 1977.
DOI : 10.1086/jar.33.4.3629752

L. A. Zadeh, Fuzzy sets, Information and Control, vol.8, issue.3, pp.338-353, 1965.
DOI : 10.1016/S0019-9958(65)90241-X

S. Zhang, R. Wang, and X. Zhang, Identification of overlapping community structure in complex networks using fuzzy -means clustering, Physica A: Statistical Mechanics and its Applications, pp.483-490, 2007.
DOI : 10.1016/j.physa.2006.07.023

X. Zhang and Z. Xu, Hesitant fuzzy agglomerative hierarchical clustering algorithms, International Journal of Systems Science, vol.7, issue.3, pp.562-576, 2015.
DOI : 10.1016/S0019-9958(65)90241-X

Z. Zhang, Community structure detection in complex networks with partial background information, EPL (Europhysics Letters), vol.101, issue.4, p.48005, 2013.
DOI : 10.1209/0295-5075/101/48005

H. Zhou, Distance, dissimilarity index, and network community structure, Physical Review E, vol.67, issue.6, p.61901, 2003.
DOI : 10.1103/PhysRevE.67.061901

K. Zhou, A. Martin, and Q. Pan, Evidential Communities for Complex Networks, Information Processing and Management of Uncertainty in Knowledge- Based Systems, pp.557-566, 2014.
DOI : 10.1007/978-3-319-08795-5_57

URL : https://hal.archives-ouvertes.fr/hal-01101172

K. Zhou, A. Martin, and Q. Pan, A similarity-based community detection method with multiple prototype representation. Physica A: Statistical Mechanics and its Applications, pp.519-531, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01185866

K. Zhou, A. Martin, Q. Pan, and L. , Evidential relational clustering using medoids, 18th International Conference on Information Fusion, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01176143

K. Zhou, A. Martin, Q. Pan, and L. , Median evidential c-means algorithm and its application to community detection. Knowledge-Based Systems, Z.-g, vol.74, pp.69-88, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01100902

T. Zhou, L. Lü, and Y. Zhang, Predicting missing links via local information, The European Physical Journal B, vol.30, issue.4, pp.623-630, 2009.
DOI : 10.1140/epjb/e2009-00335-8

X. Zhu, Semi-supervised learning literature survey, 2006.

X. Zhu and A. B. Goldberg, Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning, pp.1-130, 2009.

X. Zhu, J. Lafferty, and R. Rosenfeld, Semi-supervised learning with graphs, 2005.