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
Fast Algorithms for Mining Association Rules, Proc. of Int. Conf. on Very Large Data Bases (VLDB), pp.487-499, 1994. ,
Mining association rules between sets of items in large databases, Proc. of Int. Conf. on Management of Data (SIGMOD), pp.207-216 ,
Gephi : An Open Source Software for Exploring and Manipulating Networks, Proc. of Int. AAAI Conf. on Weblogs and Social Media, pp.361-362, 2009. ,
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
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
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
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
Fast unfolding of communities in large networks. Statistical Mechanics: Theory and Experiment, pp.10008-110, 2008. ,
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
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. ,
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
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
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
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
-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
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
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
Clique Percolation in Random Networks, Physical Review Letters, vol.94, issue.16, pp.2-5, 2005. ,
DOI : 10.1103/PhysRevLett.94.160202
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
On Random Graphs, Publicationes Mathematicae, vol.6, pp.290-297, 1959. ,
A combinatorial problem in geometry, Compositio Mathematica, vol.2, pp.463-470, 1935. ,
-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
Solutio problematis ad geometriam situs pertinentis Commentarii academiae scientiarum Petropolitanae, pp.128-140 ,
A set of measures of centrality based upon betweenness, Sociometry, vol.40, issue.1, pp.35-41, 1977. ,
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
Formal Concept Analysis: Foundations and Applications, 2005. ,
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
Tiling Databases, Proc. of Discovery Science (DS), pp.278-289 ,
DOI : 10.1007/978-3-540-30214-8_22
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
Algorithmic graph theory, 1982. ,
Frequent Set Mining In The Data Mining and Knowledge Discovery Handbook, pp.377-397 ,
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
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
Data Mining, 2005. ,
DOI : 10.1007/978-1-4899-7993-3_104-2
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
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
From molecular to modular cell biology, Nature, vol.83, issue.6761, pp.402-449, 1999. ,
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
A database perspective on knowledge discovery, Communications of the ACM, vol.39, issue.11, pp.58-64, 1996. ,
DOI : 10.1145/240455.240472
STRING 8 -a global view on proteins and their functional interactions in 630 organisms, Nucleic acids research, vol.37, pp.412-416, 2009. ,
Clustering by means of medoids. Statistical Data Analysis Based on the L1 Norm, pp.405-416, 1987. ,
Finding Groups in Data: an introduction to cluster analysis, 1990. ,
DOI : 10.1002/9780470316801
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
Pattern Teams, Proc. of European Conf. on Machine Learning and Princ. and Pract. of Knowledge Discovery in Databases (ECML/PKDD), pp.577-584, 2006. ,
A sequential algorithm for fast clique percolation, Physical Review E, vol.78, issue.2, pp.1-8, 2008. ,
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
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
Relational models for generating labeled real-world graphs, Proc. of Int. Workshop on Mining and Learning with Graphs, pp.1-3, 2009. ,
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
A method of matrix analysis of group structure, Psychometrika, vol.14, issue.2, pp.95-116, 1949. ,
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
Structural Correlation Pattern Mining for Large Graphs, 2010. ,
Machine Learning, 1997. ,
On cliques in graphs, Israel Journal of Mathematics, vol.3, issue.1, pp.23-28, 1965. ,
DOI : 10.1007/BF02760024
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
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
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
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
Extraction sous Contraintes d'Ensembles de Cliques Homogènes, Proc. of Extraction et Gestion de la Connaissance (EGC), pp.443-454, 2011. ,
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
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
Properties of Bridge Nodes in Social Networks, Proc. of Int. Conf. on Computational Collective Intelligence (ICCCI), pp.357-364, 2009. ,
DOI : 10.1017/CBO9780511815478
L2L: a simple tool for discovering the hidden significance in microarray expression data, Genome Biology, vol.6, issue.9, pp.1-18, 2005. ,
The Structure and Function of Complex Networks, SIAM Review, vol.45, issue.2, p.167, 2003. ,
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
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. ,
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
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
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. ,
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
Constraint-Based Pattern Set Mining, Proc. of SIAM Int. Conf. on Data Mining (SDM), pp.237-248, 2007. ,
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
The centrality index of a graph, Psychometrika, vol.24, issue.66, pp.581-603, 1966. ,
DOI : 10.1007/BF02289527
Dense itemsets, Proc. of Int. Conf. on Knowledge discovery and Data Mining (KDD), pp.683-688, 2004. ,
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
Item Sets That Compress, Proc. of SIAM Int. Conf. on Data Mining (SDM), pp.393-404, 2006. ,
DOI : 10.1137/1.9781611972764.35
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
Mining attribute-structure correlated patterns in large attributed graphs, Proc. of the VLDB Endowment, pp.466-477, 2012. ,
DOI : 10.14778/2140436.2140443
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
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
Introduction to Data Mining, 2005. ,
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
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
Identification of functional modules using network topology and high-throughput data, BMC Systems Biology, vol.1, issue.83, pp.1-17, 2007. ,
Serial Analysis of Gene Expression, Science, vol.87, issue.2705235, pp.484-487, 1995. ,
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. ,
Co-expression networks: graph properties and topological comparisons, Bioinformatics, vol.26, issue.2, pp.205-214, 2010. ,
DOI : 10.1093/bioinformatics/btp632
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
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
Theoretical Foundations of Association Rules, Proc. of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp.1-8, 1998. ,
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
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
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
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