J. Abello, M. G. Resende, and S. Sudarsky, Massive Quasi-Clique Detection, Proc. of Latin American Symposium on Theoretical Informatics, pp.598-612, 2002.
DOI : 10.1007/3-540-45995-2_51

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.4659

R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, Proc. of Int. Conf. on Very Large Data Bases (VLDB), pp.487-499, 1994.

R. Agrawal, T. Imielinski, and A. Swami, Mining association rules between sets of items in large databases, Proc. of Int. Conf. on Management of Data (SIGMOD), pp.207-216

S. Mathieu-bastian, M. Heymann, and . Jacomy, Gephi : An Open Source Software for Exploring and Manipulating Networks, Proc. of Int. AAAI Conf. on Weblogs and Social Media, pp.361-362, 2009.

Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal, Mining frequent patterns with counting inference, ACM SIGKDD Explorations Newsletter, vol.2, issue.2, pp.66-75, 2000.
DOI : 10.1145/380995.381017

URL : https://hal.archives-ouvertes.fr/hal-00467750

C. Becquet, S. Blachon, B. Jeudy, J. Boulicaut, and O. Gandrillon, Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data, Genome Biology, vol.3, issue.12, pp.1-16, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00194295

N. Benchettara, R. Kanawati, and C. Rouveirol, A supervised machine learning link prediction approach for academic collaboration recommendation, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.253-256, 2010.
DOI : 10.1145/1864708.1864760

M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, and D. Pedreschi, Foundations of Multidimensional Network Analysis, 2011 International Conference on Advances in Social Networks Analysis and Mining, pp.485-489, 2011.
DOI : 10.1109/ASONAM.2011.103

D. Vincent, J. Blondel, R. Guillaume, E. Lambiotte, and . Lefebvre, Fast unfolding of communities in large networks. Statistical Mechanics: Theory and Experiment, pp.10008-110, 2008.

F. Bonchi and C. Lucchese, Pushing Tougher Constraints in Frequent Pattern Mining, Proc. of European Conf. on Machine Learning and Princ. and Pract. of Knowledge Discovery in Databases (ECML/PKDD), pp.114-124, 2005.
DOI : 10.1007/11430919_15

F. Bonchi, F. Giannotti, A. Mazzanti, and D. Pedreschi, Exante: Anticipated data reduction in constrained pattern mining) [12] Francesco Bonchi, Fosca Giannotti, Alessio Mazzanti, and Dino Pedreschi. Efficient breadth-first mining of frequent pattern with monotone constraints, Proc. of European Conf. on Machine Learning and Princ. and Pract. of Knowledge Discovery in Databases, pp.59-70131, 2003.

J. Boulicaut, A. Bykowski, and C. Rigotti, Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries, Data Mining and Knowledge Discovery, vol.7, issue.1, pp.5-22, 2003.
DOI : 10.1023/A:1021571501451

URL : https://hal.archives-ouvertes.fr/hal-01503814

B. Bringmann and A. Zimmermann, One in a million: picking the right patterns, Knowledge and Information Systems, vol.6, issue.3, pp.61-81, 2009.
DOI : 10.1007/s10115-008-0136-4

D. Burdick, M. Calimlim, and J. Gehrke, MAFIA: a maximal frequent itemset algorithm for transactional databases, Proceedings 17th International Conference on Data Engineering, pp.443-452, 2001.
DOI : 10.1109/ICDE.2001.914857

T. Calders and B. Goethals, Mining All Non-derivable Frequent Itemsets, Proc. of European Conf. on Machine Learning and Princ. and Pract. of Knowledge Discovery in Databases (ECM- L/PKDD), pp.74-85, 2002.
DOI : 10.1007/3-540-45681-3_7

L. Cerf, J. Besson, C. Robardet, and J. Boulicaut, -ary Relations, Proc. of SIAM Int. Conf. on Data Mining (SDM), pp.37-48, 2008.
DOI : 10.1137/1.9781611972788.4

URL : https://hal.archives-ouvertes.fr/hal-01408836

H. Cheng, S. Philip, J. Yu, and . Han, Approximate Frequent Itemset Mining In the Presence of Random Noise, Soft Computing for Knowledge Discovery and Data Mining, pp.363-389
DOI : 10.1007/978-0-387-69935-6_15

M. Coscia, F. Giannotti, and D. Pedreschi, A classification for community discovery methods in complex networks, Statistical Analysis and Data Mining, vol.78, issue.5, pp.512-546, 2011.
DOI : 10.1002/sam.10133

I. Derényi, G. Palla, and T. Vicsek, Clique Percolation in Random Networks, Physical Review Letters, vol.94, issue.16, pp.2-5, 2005.
DOI : 10.1103/PhysRevLett.94.160202

N. Du, B. Wu, X. Pei, B. Wang, and L. Xu, Community detection in large-scale social networks, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis , WebKDD/SNA-KDD '07, pp.16-25, 2007.
DOI : 10.1145/1348549.1348552

P. Erdös and A. Rényi, On Random Graphs, Publicationes Mathematicae, vol.6, pp.290-297, 1959.

P. Erdös and G. Szekeres, A combinatorial problem in geometry, Compositio Mathematica, vol.2, pp.463-470, 1935.

M. Ester, R. Ge, J. Byron, Z. Gao, B. Hu et al., -Center Problem, ACM Transactions on Knowledge Discovery from Data (TKDD), vol.2, issue.2, pp.1-35, 2008.
DOI : 10.1137/1.9781611972764.22

URL : https://hal.archives-ouvertes.fr/halshs-01362486

L. Euler, Solutio problematis ad geometriam situs pertinentis Commentarii academiae scientiarum Petropolitanae, pp.128-140

C. Linton and . Freeman, A set of measures of centrality based upon betweenness, Sociometry, vol.40, issue.1, pp.35-41, 1977.

M. Fukuzaki, M. Seki, H. Kashima, and J. Sese, Finding Itemset-Sharing Patterns in a Large Itemset-Associated Graph, Proc. of Pacific-Asia Conf. on Knowl. Discov. and Data Mining (PAKDD), pp.147-159, 2010.
DOI : 10.1007/978-3-642-13672-6_15

B. Ganter, G. Stumme, and R. Wille, Formal Concept Analysis: Foundations and Applications, 2005.

W. Gao, K. Wong, Y. Xia, and R. Xu, Clique Percolation Method for Finding Naturally Cohesive and Overlapping Document Clusters, Proc. of Int. Conf. on Computer Processing of Oriental Languages (ICCPOL), pp.97-108, 2006.
DOI : 10.1007/11940098_10

F. Geerts, B. Goethals, and T. Mielikäinen, Tiling Databases, Proc. of Discovery Science (DS), pp.278-289
DOI : 10.1007/978-3-540-30214-8_22

A. Gély, L. Nourine, and B. Sadi, Enumeration aspects of maximal cliques and bicliques, Discrete Applied Mathematics, vol.157, issue.7, pp.1447-1459, 2009.
DOI : 10.1016/j.dam.2008.10.010

A. Gibbons, Algorithmic graph theory, 1982.

B. Goethals, Frequent Set Mining In The Data Mining and Knowledge Discovery Handbook, pp.377-397

K. Gouda and M. J. Zaki, GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets, Data Mining and Knowledge Discovery, vol.129, issue.2, pp.1-20, 2005.
DOI : 10.1007/s10618-005-0002-x

S. Günnemann, B. Boden, and T. Seidl, DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors, Proc. of European Conf. on Machine Learning and Princ. and Pract. of Knowledge Discovery in Databases (ECML/PKDD), pp.565-580, 2011.
DOI : 10.1007/978-3-642-23780-5_46

J. Han and M. Kamber, Data Mining, 2005.
DOI : 10.1007/978-1-4899-7993-3_104-2

J. Han, J. Pei, and Y. Yin, Mining Frequent Patterns without Candidate Generation, Proc. of Int. Conf. on Management of Data (SIGMOD), pp.1-12, 2000.
DOI : 10.1145/335191.335372

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.2678

D. Hanisch, A. Zien, R. Zimmer, and T. Lengauer, Co-clustering of biological networks and gene expression data, Bioinformatics, vol.18, issue.Suppl 1, pp.145-154, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S145

H. Leland, . Hartwell, J. John, S. Hopfield, . Leibler et al., From molecular to modular cell biology, Nature, vol.83, issue.6761, pp.402-449, 1999.

J. Hipp, U. Güntzer, and G. Nakhaeizadeh, Algorithms for association rule mining --- a general survey and comparison, ACM SIGKDD Explorations Newsletter, vol.2, issue.1, pp.58-64, 2000.
DOI : 10.1145/360402.360421

T. Imielinski and H. Mannila, A database perspective on knowledge discovery, Communications of the ACM, vol.39, issue.11, pp.58-64, 1996.
DOI : 10.1145/240455.240472

J. Lars, M. Jensen, M. Kuhn, S. Stark, C. Chaffron et al., STRING 8 -a global view on proteins and their functional interactions in 630 organisms, Nucleic acids research, vol.37, pp.412-416, 2009.

L. Kaufman and P. Rousseeuw, Clustering by means of medoids. Statistical Data Analysis Based on the L1 Norm, pp.405-416, 1987.

L. Kaufman and P. Rousseeuw, Finding Groups in Data: an introduction to cluster analysis, 1990.
DOI : 10.1002/9780470316801

A. Khan, X. Yan, and K. Wu, Towards proximity pattern mining in large graphs, Proceedings of the 2010 international conference on Management of data, SIGMOD '10, pp.867-878, 2010.
DOI : 10.1145/1807167.1807261

J. Arno, E. K. Knobbe, and . Ho, Pattern Teams, Proc. of European Conf. on Machine Learning and Princ. and Pract. of Knowledge Discovery in Databases (ECML/PKDD), pp.577-584, 2006.

M. Jussi, M. Kumpula, K. Kivela, J. Kaski, and . Saramaki, A sequential algorithm for fast clique percolation, Physical Review E, vol.78, issue.2, pp.1-8, 2008.

J. Leyritz, S. Schicklin, S. Blachon, C. Keime, C. Robardet et al., SQUAT: A web tool to mine human, murine and avian SAGE data, BMC Bioinformatics, vol.9, issue.1, pp.1-12, 2008.
DOI : 10.1186/1471-2105-9-378

URL : https://hal.archives-ouvertes.fr/hal-00425133

Z. Liang, M. Xu, M. Teng, and L. Niu, Comparison of protein interaction networks reveals species conservation and divergence, BMC Bioinformatics, vol.7, issue.1, p.457, 2006.
DOI : 10.1186/1471-2105-7-457

C. Lippert, N. Shervashidze, and O. Stegle, Relational models for generating labeled real-world graphs, Proc. of Int. Workshop on Mining and Learning with Graphs, pp.1-3, 2009.

G. Liu and L. Wong, Effective Pruning Techniques for Mining Quasi-Cliques, Proc. of European Conf. on Machine Learning and Princ. and Pract. of Knowledge Discovery in Databases (ECML/PKDD), pp.33-49, 2008.
DOI : 10.1007/978-3-540-87481-2_3

R. , D. Luce, D. Albert, and . Perry, A method of matrix analysis of group structure, Psychometrika, vol.14, issue.2, pp.95-116, 1949.

K. Makino and T. Uno, New Algorithms for Enumerating All Maximal Cliques, Proc. of Scandinavian Workshop on Algorithm Theory (SWAT), pp.260-272, 2004.
DOI : 10.1007/978-3-540-27810-8_23

M. Wagner-jr, Structural Correlation Pattern Mining for Large Graphs, 2010.

M. Tom and . Mitchell, Machine Learning, 1997.

J. W. Moon and L. Moser, On cliques in graphs, Israel Journal of Mathematics, vol.3, issue.1, pp.23-28, 1965.
DOI : 10.1007/BF02760024

F. Moser, R. Ge, and M. Ester, Joint cluster analysis of attribute and relationship data withouta-priori specification of the number of clusters, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.510-519, 2007.
DOI : 10.1145/1281192.1281248

F. Moser, R. Colak, A. Rafiey, and M. Ester, Mining Cohesive Patterns from Graphs with Feature Vectors, Proc. of SIAM Int. Conf. on Data Mining (SDM), pp.593-604, 2009.
DOI : 10.1137/1.9781611972795.51

P. Mougel, M. Plantevit, C. Rigotti, O. Gandrillon, and J. Boulicaut, Constraint-Based Mining of Sets of Cliques Sharing Vertex Properties, Proc. of Workshop on Analysis of Complex NEtworks (ACNE) co-located with ECML/PKDD, pp.1-14, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01381539

P. Mougel, M. Plantevit, C. Rigotti, O. Gandrillon, and J. Boulicaut, A Data Mining Approach to Highlight Relations Between Functional Modules, Proc. of Integrative Post-Genomics (IPG), p.1, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01381584

P. Mougel, M. Plantevit, C. Rigotti, O. Gandrillon, and J. Boulicaut, Extraction sous Contraintes d'Ensembles de Cliques Homogènes, Proc. of Extraction et Gestion de la Connaissance (EGC), pp.443-454, 2011.

P. Mougel, C. Rigotti, and O. Gandrillon, Finding Collections of k-Clique Percolated Components in Attributed Graphs, Proc. of Pacific-Asia Conf. on Knowl. Discov . and Data Mining (PAKDD), pp.181-192
DOI : 10.1007/978-3-642-30220-6_16

URL : https://hal.archives-ouvertes.fr/hal-00758843

P. Mougel, C. Rigotti, and O. Gandrillon, Finding Collections of Protein Modules in Protein-Protein Interaction Networks, Proc. of Bioinformatics and Computational Biology (BiCOB), pp.1-7
URL : https://hal.archives-ouvertes.fr/hal-00758858

K. Musial and K. Juszczyszyn, Properties of Bridge Nodes in Social Networks, Proc. of Int. Conf. on Computational Collective Intelligence (ICCCI), pp.357-364, 2009.
DOI : 10.1017/CBO9780511815478

C. John, A. M. Newman, and . Weiner, L2L: a simple tool for discovering the hidden significance in microarray expression data, Genome Biology, vol.6, issue.9, pp.1-18, 2005.

E. Mark and . Newman, The Structure and Function of Complex Networks, SIAM Review, vol.45, issue.2, p.167, 2003.

G. Palla and I. Derényi, Uncovering the overlapping community structure of complex networks in nature and society, Nature, vol.387, issue.7043, pp.814-818, 2005.
DOI : 10.1038/nature03248

G. Palla and I. Derényi, Illés Farkas, and Tamás Vicsek Uncovering the overlapping community structure of complex networks in nature and society -Supplementary information, Nature, vol.435, issue.7043, 2005.

J. Pandey, M. Koyuturk, and A. Grama, Functional characterization and topological modularity of molecular interaction networks, BMC Bioinformatics, vol.11, issue.Suppl 1, p.35, 2010.
DOI : 10.1186/1471-2105-11-S1-S35

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering Frequent Closed Itemsets for Association Rules, Proc. of Int. Conf. on Database Theory (ICDT), pp.398-416, 1999.
DOI : 10.1007/3-540-49257-7_25

URL : https://hal.archives-ouvertes.fr/hal-00467747

J. Pei, J. Han, and R. Mao, CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets, Proc. of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp.21-30, 2000.

S. Quiniou, P. Cellier, T. Charnois, and D. Legallois, Fouille de graphes sous contraintes linguistiques pour l'exploration de grands textes, Proc. of Traitement Automatique des Langues Naturelles (TALN), pp.253-266
URL : https://hal.archives-ouvertes.fr/hal-00702606

L. De, R. , and A. Zimmermann, Constraint-Based Pattern Set Mining, Proc. of SIAM Int. Conf. on Data Mining (SDM), pp.237-248, 2007.

C. Robardet, Constraint-Based Pattern Mining in Dynamic Graphs, 2009 Ninth IEEE International Conference on Data Mining, pp.950-955, 2009.
DOI : 10.1109/ICDM.2009.99

URL : https://hal.archives-ouvertes.fr/hal-01437815

G. Sabidussi, The centrality index of a graph, Psychometrika, vol.24, issue.66, pp.581-603, 1966.
DOI : 10.1007/BF02289527

K. Jouni, H. Seppänen, and . Mannila, Dense itemsets, Proc. of Int. Conf. on Knowledge discovery and Data Mining (KDD), pp.683-688, 2004.

J. Sese, M. Seki, and M. Fukuzaki, Mining networks with shared items, Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10, pp.1681-1684, 2010.
DOI : 10.1145/1871437.1871703

A. Siebes, J. Vreeken, and M. Van-leeuwen, Item Sets That Compress, Proc. of SIAM Int. Conf. on Data Mining (SDM), pp.393-404, 2006.
DOI : 10.1137/1.9781611972764.35

A. Silva, M. J. Wagner-jr-meira, and . Zaki, Structural correlation pattern mining for large graphs, Proceedings of the Eighth Workshop on Mining and Learning with Graphs, MLG '10, pp.119-126, 2010.
DOI : 10.1145/1830252.1830268

A. Silva, M. J. Wagner-jr-meira, and . Zaki, Mining attribute-structure correlated patterns in large attributed graphs, Proc. of the VLDB Endowment, pp.466-477, 2012.
DOI : 10.14778/2140436.2140443

A. Soulet and B. Crémilleux, An Efficient Framework for Mining Flexible Constraints, Proc. of Pacific-Asia Conf. on Knowl. Discov. and Data Mining, pp.661-671
DOI : 10.1007/11430919_76

URL : https://hal.archives-ouvertes.fr/hal-00324837

X. Sun, B. Andrew, and . Nobel, Significance and Recovery of Block Structures in Binary Matrices with Noise, Proc. of Conf. on Learning Theory (COLT), pp.109-122, 2006.
DOI : 10.1007/11776420_11

P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 2005.

E. Tomita, A. Tanaka, and H. Takahashi, The worst-case time complexity for generating all maximal cliques and computational experiments, Theoretical Computer Science, vol.363, issue.1, pp.28-42, 2006.
DOI : 10.1016/j.tcs.2006.06.015

H. Tong, B. Gallagher, C. Faloutsos, and T. Eliassi-rad, Fast best-effort pattern matching in large attributed graphs, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.737-746, 2007.
DOI : 10.1145/1281192.1281271

I. Ulitsky and R. Shamir, Identification of functional modules using network topology and high-throughput data, BMC Systems Biology, vol.1, issue.83, pp.1-17, 2007.

E. Victor, L. Velculescu, B. Zhang, . Vogelstein, W. Kenneth et al., Serial Analysis of Gene Expression, Science, vol.87, issue.2705235, pp.484-487, 1995.

L. Xu and I. King, A PCA approach for fast retrieval of structural patterns in attributed graphs, IEEE Trans. on Systems, Man, and Cybernetics, vol.31, issue.5, pp.812-817, 2001.

R. Xulvi, -. Brunet, and H. Li, Co-expression networks: graph properties and topological comparisons, Bioinformatics, vol.26, issue.2, pp.205-214, 2010.
DOI : 10.1093/bioinformatics/btp632

G. Yang, The complexity of mining maximal frequent itemsets and maximal frequent patterns, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.344-353, 2004.
DOI : 10.1145/1014052.1014091

M. Zaki, Scalable algorithms for association mining, IEEE Transactions on Knowledge and Data Engineering, vol.12, issue.3, pp.372-390, 2000.
DOI : 10.1109/69.846291

M. Javeed, Z. , and M. Ogihara, Theoretical Foundations of Association Rules, Proc. of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp.1-8, 1998.

Z. Zeng, J. Wang, L. Zhou, and G. Karypis, Coherent closed quasi-clique discovery from large dense graph databases, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.797-802, 2006.
DOI : 10.1145/1150402.1150506

S. Zhang, X. Ning, and X. Zhang, Identification of functional modules in a PPI network by clique percolation clustering, Computational Biology and Chemistry, vol.30, issue.6, pp.445-451, 2006.
DOI : 10.1016/j.compbiolchem.2006.10.001

Y. Zhou, H. Cheng, J. Xu, and Y. , Graph clustering based on structural/attribute similarities, Proc. of Int. Conf. on Very Large Data Bases (VLDB), pp.718-729, 2009.
DOI : 10.14778/1687627.1687709

Y. Zhou, H. Cheng, J. Xu, and Y. , Clustering Large Attributed Graphs: An Efficient Incremental Approach, 2010 IEEE International Conference on Data Mining, pp.689-698, 2010.
DOI : 10.1109/ICDM.2010.41

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.642.96