H. Abdelhaq, C. Sengstock, and M. Gertz, Eventweet: Online localized event detection from twitter, Proc. VLDB Endow, vol.6, issue.12, pp.1326-1329, 2013.

H. Abdelhaq, C. Sengstock, and M. Gertz, Eventweet: Online localized event detection from twitter, PVLDB, vol.6, issue.12, pp.1326-1329, 2013.

J. Abello, M. G. Resende, and S. Sudarsky, Massive quasi-clique detection, LATIN 2002: Theoretical Informatics, 5th Latin American Symposium, pp.598-612, 2002.

F. Adriaens, J. Lij, and T. Bie, Subjectively interesting connecting trees, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2017, pp.53-69, 2017.

C. Aggarwal and K. Subbian, Event detection in social streams, SIAM DM, pp.624-635, 2012.

R. Agrawal and R. Srikant, Mining sequential patterns, Proceedings of the Eleventh International Conference on Data Engineering, pp.3-14, 1995.

R. Agrawal, T. Imielinski, and A. N. Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp.207-216, 1993.

R. Ahmed and G. Karypis, Algorithms for mining the evolution of conserved relational states in dynamic networks, Knowl. Inf. Syst, vol.33, issue.3, pp.603-630, 2012.

G. Luca-maria-aiello, C. Petkos, D. Martin, S. Corney, R. Papadopoulos et al., Ioannis Kompatsiaris, and Alejandro Jaimes. Sensing trending topics in twitter, Trans. Multi, vol.15, issue.6, p.143, 2013.

M. Akbari, X. Hu, L. Nie, and T. Chua, From tweets to wellness: Wellness event detection from twitter streams, AAAI, vol.87, pp.87-93, 2016.

E. A. Akkoyunlu, The enumeration of maximal cliques of large graphs, SIAM J. Comput, vol.2, issue.1, pp.1-6, 1973.

L. Akoglu, H. Tong, B. Meeder, and C. Faloutsos, PICS: parameter-free identification of cohesive subgroups in large attributed graphs, Proceedings of the Twelfth SIAM International Conference on Data Mining, pp.439-450, 2012.

R. Albert and A. Barabási, Statistical mechanics of complex networks, 2001.

J. M. Aronis, F. J. Provost, and B. G. Buchanan, Exploiting background knowledge in automated discovery, KDD, pp.355-358, 1996.

M. Atzmueller, S. Doerfel, and F. Mitzla, Description-oriented community detection using exhaustive subgroup discovery, Inf. Sci, vol.329, pp.965-984, 2016.

M. Atzmüller and F. Puppe, Sd-map -A fast algorithm for exhaustive subgroup discovery, Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp.6-17, 2006.

D. Gary, C. W. Bader, and . Hogue, An automated method for finding molecular complexes in large protein interaction networks, BMC Bioinformatics, vol.4, issue.2, 2003.

A. Barabási and E. Bonabeau, Scale-free networks, Scientific american, vol.288, issue.5, pp.60-69, 2003.

D. Stephen, M. J. Bay, and . Pazzani, Detecting group di erences: Mining contrast sets, Data Min. Knowl. Discov, vol.5, issue.3, pp.213-246, 2001.

H. Becker, M. Naaman, and L. Gravano, Beyond trending topics: Real-world event identification on twitter, ICWSM, vol.87, 2011.

T. De-beéck, A. Hommersom, J. Van-haaren, M. Van-der-heijden, J. Davis et al., Mining hierarchical pathology data using inductive logic programming, AIME, pp.76-85, 2015.

A. Belfodil, A. Belfodil, and M. Kaytoue, Anytime subgroup discovery in numerical domains with guarantees, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2018, pp.500-516, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02117627

A. Bendimerad, M. Plantevit, and C. Robardet, Unsupervised exceptional attributed sub-graph mining in urban data, IEEE 16th International Conference on Data Mining, ICDM 2016, pp.21-30, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01430622

A. Bendimerad, M. Plantevit, and C. Robardet, Mining exceptional closed patterns in attributed graphs, Knowl. Inf. Syst, vol.56, issue.1, pp.1-25, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01625007

A. Bendimerad, A. Mel, J. Lij-jt, M. Plantevit, C. Robardet et al., Mining subjectively interesting attributed subgraphs, Proceedings of the 14th Workshop on Mining and Learning with Graphs, MLG '18, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02060190

A. Bendimerad, J. Lij-jt, M. Plantevit, C. Robardet, and T. Bie, Contrastive antichains in hierarchies, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02114775

A. Bendimerad, M. Plantevit, C. Robardet, and S. Amer-yahia, User-driven geolocated event detection in social media, IEEE Transactions on Knowledge and Data Engineering, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02272082

M. Berlingerio, F. Bonchi, B. Bringmann, and A. Gionis, Mining graph evolution rules, Machine Learning and Knowledge Discovery in Databases, pp.115-130, 2009.

K. S. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, When is "nearest neighbor" meaningful?, Database Theory -ICDT '99, 7th International Conference, p.17, 1999.

M. Bhuiyan and M. A. Hasan, Interactive knowledge discovery from hidden data through sampling of frequent patterns, Statistical Analysis and Data Mining, vol.9, issue.4, pp.205-229, 2016.

A. Mansurul, M. A. Bhuiyan, and . Hasan, PRIIME: A generic framework for interactive personalized interesting pattern discovery, 2016 IEEE International Conference on Big Data, pp.606-615, 2016.

C. M. Bishop, Graphical models. Pattern recognition and machine learning, vol.127, p.126, 2007.

D. Vincent, J. Blondel, R. Guillaume, E. Lambiotte, and . Lefebvre, Fast unfolding of communities in large networks, Journal of statistical mechanics: theory and experiment, issue.10, p.10008, 2008.

B. Boden, S. Günnemann, H. Ho-mann, and T. Seidl, Mining coherent subgraphs in multi-layer graphs with edge labels, The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1258-1266, 2012.

A. Bojchevski and S. Günnemann, Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), pp.2738-2745, 2018.

M. Boley, T. Horváth, A. Poigné, and S. Wrobel, Listing closed sets of strongly accessible set systems with applications to data mining, Theor. Comput. Sci, vol.411, issue.3, pp.691-700, 2010.

M. Boley, C. Lucchese, D. Paurat, and T. Gärtner, Direct local pattern sampling by e cient two-step random procedures, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.582-590, 2011.

M. Boley, S. Moens, and T. Gärtner, Linear space direct pattern sampling using coupling from the past, The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pp.69-77, 2012.

F. Bonchi and C. Lucchese, Extending the state-of-the-art of constraint-based pattern discovery, Data Knowl. Eng, vol.60, issue.2, pp.377-399, 2007.

F. Bonchi, A. Gionis, F. Gullo, and A. Ukkonen, Chromatic correlation clustering, The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pp.1321-1329, 2012.

K. M. Borgwardt, H. Kriegel, and P. Wackersreuther, Pattern mining in frequent dynamic subgraphs, Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), pp.818-822, 2006.

G. Bosc, Anytime discovery of a diverse set of patterns with Monte Carlo tree search. (Découverte d'un ensemble diversifié de motifs avec la recherche arborescente de Monte Carlo), vol.23, p.22, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01418663

G. Bosc, J. Boulicaut, C. Raïssi, and M. Kaytoue, Anytime discovery of a diverse set of patterns with monte carlo tree search, Data Min. Knowl. Discov, vol.32, issue.3, pp.604-650, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01418663

C. Bothorel, J. D. Cruz, M. Magnani, and B. Micenková, Clustering attributed graphs: Models, measures and methods, Network Science, vol.3, issue.3, pp.408-444, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01257833

J. Boulicaut, A. Bykowski, and C. Rigotti, Free-sets: A condensed representation of boolean data for the approximation of frequency queries, Data Min. Knowl. Discov, vol.7, issue.1, pp.5-22, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01503814

J. Boulicaut, M. Plantevit, and C. Robardet, Local pattern detection in attributed graphs, Solving Large Scale Learning Tasks. Challenges and Algorithms -Essays Dedicated to
URL : https://hal.archives-ouvertes.fr/hal-02016506

, Katharina Morik on the Occasion of Her 60th Birthday, pp.168-183, 2016.

S. Boyd and L. Vandenberghe, Convex optimization, 2004.

B. Bringmann and S. Nijssen, What is frequent in a single graph?, Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, pp.858-863, 2008.

C. Bron and J. Kerbosch, Finding all cliques of an undirected graph (algorithm 457)

, Commun. ACM, vol.16, issue.9, pp.575-576, 1973.

D. Bu, Y. Zhao, L. Cai, H. Xue, X. Zhu et al., Topological structure analysis of the protein-protein interaction network in budding yeast, Nucleic acids research, vol.31, issue.9, pp.2443-2450, 2003.

H. Cao, N. Mamoulis, and D. W. Cheung, Mining frequent spatio-temporal sequential patterns, Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp.82-89, 2005.

M. Ceccarello, C. Fantozzi, A. Pietracaprina, G. Pucci, and F. Vandin, Clustering uncertain graphs. PVLDB, vol.11, issue.4, pp.472-484, 2017.

F. Chen and D. B. Neill, Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs, KDD '14, pp.978-979, 2014.

L. Chen and A. Roy, Event detection from flickr data through wavelet-based spatial analysis, CIKM, pp.523-532, 2009.

Y. Chi, Y. Yang, and R. R. Muntz, Indexing and mining free trees, Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), pp.509-512, 2003.

Y. Chi, R. R. Muntz, S. Nijssen, and J. N. Kok, Frequent subtree mining -an overview, Fundam. Inform, vol.66, issue.1-2, pp.161-198, 2005.

M. Cho, J. Pei, H. Wang, and W. Wang, Preference-based frequent pattern mining, vol.IJDWM, p.27, 2005.

M. Thomas, . Cover, A. Joy, and . Thomas, Entropy, relative entropy and mutual information, vol.2, pp.1-55, 1991.

. Tijl-de-bie, An information theoretic framework for data mining, Proceedings of the 17th

, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.564-572, 2011.

. Tijl-de-bie, Maximum entropy models and subjective interestingness: an application to tiles in binary databases, Data Min. Knowl. Discov, vol.23, issue.3, pp.407-446, 2011.

C. Rebelo-de-sá, W. Duivesteijn, P. J. Azevedo, A. Mário-jorge, C. Soares et al., Discovering a taste for the unusual: exceptional models for preference mining, Machine Learning, vol.107, pp.1775-1807, 2018.

M. Jesús, P. González, F. Herrera, and M. Mesonero, Evolutionary fuzzy rule induction process for subgroup discovery: A case study in marketing, IEEE Trans. Fuzzy Systems, vol.15, issue.4, pp.578-592, 2007.

J. Demsar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, vol.7, pp.1-30, 2006.

E. Desmier, M. Plantevit, C. Robardet, and J. Boulicaut, Trend mining in dynamic attributed graphs, Machine Learning and Knowledge Discovery in DatabasesEuropean Conference, ECML PKDD 2013, pp.654-669, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01339225

A. Trong-dinh-thac-do, A. Termier, B. Laurent, B. Négrevergne, S. Omidvar-tehrani et al., PGLCM: e cient parallel mining of closed frequent gradual itemsets, Knowl. Inf. Syst, vol.43, issue.3, pp.497-527, 2015.

G. Dong and J. Li, E cient mining of emerging patterns: Discovering trends and di erences, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.43-52, 1999.

X. Dong, D. Mavroeidis, F. Calabrese, and P. Frossard, Multiscale event detection in social media

. Dami, , vol.29, pp.1374-1405, 2015.

W. Duivesteijn and J. Thaele, Understanding where your classifier does (not) work -the scape model class for EMM, 2014 IEEE International Conference on Data Mining, ICDM, pp.809-814, 2014.

A. J. Wouter-duivesteijn and . Knobbe, Subgroup discovery meets bayesian networks -an exceptional model mining approach, The 10th IEEE International Conference on Data Mining, pp.158-167, 2010.

A. Wouter-duivesteijn, A. J. Feelders, and . Knobbe, Di erent slopes for di erent folks: mining for exceptional regression models with cook's distance, The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol.23, p.22, 2012.

A. Wouter-duivesteijn, A. J. Feelders, and . Knobbe, Exceptional model mining -supervised descriptive local pattern mining with complex target concepts, Data Min. Knowl. Discov, vol.30, issue.1, pp.47-98, 2016.

V. Dzyuba and M. Van-leeuwen, Interactive discovery of interesting subgroup sets, IDA, pp.150-161, 2013.

V. Dzyuba, S. Matthijs-van-leeuwen, L. Nijssen, and . Raedt, Interactive learning of pattern rankings, International Journal on Artificial Intelligence Tools, vol.23, issue.6, 2014.

D. Eppstein and D. Strash, Listing all maximal cliques in large sparse real-world graphs

, International Symposium on Experimental Algorithms, pp.364-375, 2011.

D. Eppstein, M. Lö, and D. Strash, Listing all maximal cliques in sparse graphs in near-optimal time, Algorithms and Computation -21st International Symposium, ISAAC 2010, pp.403-414, 2010.

M. Ester, R. Ge, B. J. Gao, Z. Hu, and B. Ben-moshe, Joint cluster analysis of attribute data and relationship data: the connected k-center problem, Proceedings of the Sixth SIAM International Conference on Data Mining, pp.246-257, 2006.

A. Géraud-le-falher, M. Gionis, and . Mathioudakis, Where is the soho of rome? measures and algorithms for finding similar neighborhoods in cities, Proceedings of the Ninth International Conference on Web and Social Media, ICWSM 2015, pp.228-237, 2015.

Y. Fang, R. Cheng, Y. Li, X. Chen, and J. Zhang, Exploring communities in large profiled graphs, IEEE Transactions on Knowledge & Data Engineering, issue.1, pp.1-1

Y. Fang, R. Cheng, S. Luo, and J. Hu, E ective community search for large attributed graphs, vol.9, pp.1233-1244, 2016.

Y. Fang, R. Cheng, Y. Chen, S. Luo, and J. Hu, E ective and e cient attributed community search, VLDB J, vol.26, issue.6, pp.803-828, 2017.

Y. Fang, R. Cheng, X. Li, S. Luo, and J. Hu, E ective community search over large spatial graphs, vol.10, pp.709-720, 2017.

Y. Fang, Z. Wang, R. Cheng, X. Li, S. Luo et al., On spatial-aware community search, IEEE Transactions on Knowledge and Data Engineering, 2018.

J. Fischer, S. Volker-heun, and . Kramer, Optimal string mining under frequency constraints, Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp.139-150, 2006.

G. William-flake, S. Lawrence, and C. Giles, E cient identification of web communities, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.150-160, 2000.

S. Fortunato, Community detection in graphs, Physics reports, vol.486, issue.3-5, pp.75-174, 2010.

M. Fukuzaki, M. Seki, H. Kashima, and J. Sese, Finding itemset-sharing patterns in a large itemset-associated graph, Advances in Knowledge Discovery and Data Mining, p.14

. Pacific-asia-conference, Proceedings. Part II, vol.32, pp.147-159, 2010.

D. Gamberger and N. Lavrac, Expert-guided subgroup discovery: Methodology and application, J. Artif. Intell. Res, vol.17, pp.501-527, 2002.

D. Garcia-gasulla, A. Sergioálvarez-napagao, L. Tejeda-gómez, I. Oliva-felipe, J. Gómez-sebastià et al., Social network data analysis for event detection, ECAI, pp.1009-1010, 2014.

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.

F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, Trajectory pattern mining, Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.330-339, 2007.

D. Gibson, R. Kumar, and A. Tomkins, Discovering large dense subgraphs in massive graphs, Proceedings of the 31st International Conference on Very Large Data Bases, pp.721-732, 2005.

A. Gionis, H. Mannila, T. Mielikäinen, and P. Tsaparas, Assessing data mining results via swap randomization, TKDD, vol.1, issue.3, p.14, 2007.

M. Girvan, E. J. Mark, and . Newman, Community structure in social and biological networks, Proceedings of the national academy of sciences, vol.99, pp.7821-7826, 2002.

K. Gouda and M. Zaki, E ciently mining maximal frequent itemsets, Proceedings of the 2001 IEEE International Conference on Data Mining, 2002.

A. Grigoriev, A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage t7 and the yeast saccharomyces cerevisiae, Nucleic acids research, vol.29, issue.17, pp.3513-3519, 2001.

M. Gueguen, O. Sentieys, and A. Termier, Accelerating itemset sampling using satisfiability constraints on FPGA, Design, Automation & Test in Europe Conference & Exhibition, DATE 2019, pp.1046-1051, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01941862

R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti et al., A survey of methods for explaining black box models, ACM Comput. Surv, vol.51, issue.5, 2019.

S. Günnemann, I. Färber, B. Boden, and T. Seidl, Subspace clustering meets dense subgraph mining: A synthesis of two paradigms, The 10th IEEE International Conference on Data Mining, pp.845-850, 2010.

S. Günnemann, B. Boden, and T. Seidl, DB-CSC: A density-based approach for subspace clustering in graphs with feature vectors, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2011, p.17, 2011.

S. Günnemann, B. Boden, and T. Seidl, Finding density-based subspace clusters in graphs with feature vectors, Data Min. Knowl. Discov, vol.25, issue.2, p.17, 2012.

S. Günnemann, I. Färber, S. Raubach, and T. Seidl, Spectral subspace clustering for graphs with feature vectors, 2013 IEEE 13th International Conference on Data Mining, p.17, 2013.

J. Han, J. Wang, Y. Lu, and P. Tzvetkov, Mining top-k frequent closed patterns without minimum support, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002, pp.211-218, 2002.

K. Han, F. Gui, X. Xiao, and J. Tang, Yuntian He, Zongmai Cao, and He Huang. E cient and e ective algorithms for clustering uncertain graphs, vol.12, pp.667-680, 2019.

D. Hanisch, A. Zien, R. Zimmer, and T. Lengauer, Co-clustering of biological networks and gene expression data, Proceedings of the Tenth International Conference on Intelligent Systems for Molecular Biology, pp.145-154, 2002.

P. Harremoës, Binomial and poisson distributions as maximum entropy distributions, IEEE Trans. Information Theory, vol.47, issue.5, pp.2039-2041, 2001.

A. Mohammad, M. J. Hasan, and . Zaki, Output space sampling for graph patterns, vol.PVLDB, pp.730-741, 2009.

Q. He, K. Chang, and E. Lim, Analyzing feature trajectories for event detection, SIGIR, vol.89, p.88, 2007.

P. Houdyer, A. Zimmermann, M. Kaytoue, M. Plantevit, J. Mitchell et al., Gazouille: Detecting and illustrating local events from geolocalized social media streams, ECML PKDD 2015, pp.276-280, 2015.

H. Hu, X. Yan, Y. Huang, J. Han, and X. Zhou, Mining coherent dense subgraphs across massive biological networks for functional discovery, Proceedings Thirteenth International Conference on Intelligent Systems for Molecular Biology, pp.213-221, 2005.

T. Hua, F. Chen, L. Zhao, C. Lu, and N. Ramakrishnan, STED: semisupervised targeted-interest event detectionin in twitter, ACM SIGKDD, vol.87, pp.1466-1469, 2013.

T. Hua, F. Chen, L. Zhao, C. Lu, and N. Ramakrishnan, Automatic targeteddomain spatiotemporal event detection in twitter, GeoInformatica, vol.20, issue.4, pp.765-795, 2016.

J. Huan, W. Wang, J. F. Prins, and J. Yang, SPIN: mining maximal frequent subgraphs from graph databases, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.581-586, 2004.

X. Huang, V. S. Laks, and . Lakshmanan, Attribute-driven community search, vol.10, pp.949-960, 2017.

X. Huang, V. S. Laks, J. Lakshmanan, and . Xu, Community search over big graphs: Models, algorithms, and opportunities, 33rd IEEE International Conference on Data Engineering, pp.1451-1454, 2017.

G. Ifrim, B. Shi, and I. Brigadir, Event detection in twitter using aggressive filtering and hierarchical tweet clustering, snow@WWW, vol.101, p.88, 2014.

T. Robert, . Jensen, H. Nolan, and . Miller, Gi en behavior and subsistence consumption, American Economic Review, vol.98, issue.4, pp.1553-77, 2008.

C. Jiang, F. Coenen, and M. Zito, A survey of frequent subgraph mining algorithms

, Knowledge Eng. Review, vol.28, issue.1, pp.75-105, 2013.

B. Kang, J. Lij-jt, R. Santos-rodríguez, and T. Bie, SICA: subjectively interesting component analysis, Data Min. Knowl. Discov, vol.32, issue.4, pp.949-987, 2018.

R. M. Karp, Reducibility among combinatorial problems, Proceedings of a symposium on the Complexity of Computer Computations, pp.85-103, 1972.

M. Kaytoue, S. O. Kuznetsov, and A. Napoli, Revisiting numerical pattern mining with formal concept analysis, IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp.1342-1347, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00600222

M. Kaytoue, M. Plantevit, and A. Zimmermann, Ahmed Anes Bendimerad, and Céline Robardet, Machine Learning, vol.106, pp.1171-1211, 2017.

A. Khan, X. Yan, and K. Wu, Towards proximity pattern mining in large graphs, Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, pp.867-878, 2010.

M. Kirchgessner, V. Leroy, A. Termier, S. Amer-yahia, and M. Rousset, Toppi: An e cient algorithm for item-centric mining, Big Data Analytics and Knowledge Discovery -18th International Conference, pp.19-33, 2016.

M. Klazar, Twelve countings with rooted plane trees, European J. Combin, vol.18, issue.2, pp.195-210, 1997.

W. Klösgen, Explora: A multipattern and multistrategy discovery assistant, Advances in Knowledge Discovery and Data Mining, pp.249-271, 1996.

M. Kuramochi and G. Karypis, Frequent subgraph discovery, Proceedings of the 2001 IEEE International Conference on Data Mining, pp.313-320, 2001.

S. O. Kuznetsov, Learning of simple conceptual graphs from positive and negative examples, Principles of Data Mining and Knowledge Discovery, Third European Conference, PKDD '99, pp.384-391, 1999.

N. Lavrac, P. A. Flach, and B. Zupan, Rule evaluation measures: A unifying view, Inductive Logic Programming, 9th International Workshop, ILP-99, pp.174-185, 1999.

N. Lavrac, B. Cestnik, D. Gamberger, and P. A. Flach, Decision support through subgroup discovery: Three case studies and the lessons learned, Machine Learning, vol.57, pp.115-143, 2004.

N. Lavrac, B. Kavsek, P. A. Flach, and L. Todorovski, Subgroup discovery with CN2-SD, Journal of Machine Learning Research, vol.5, pp.153-188, 2004.

N. Lavrac, A. Vavpetic, L. N. Soldatova, I. Trajkovski, and P. K. Novak, Using ontologies in semantic data mining with SEGS and g-segs, Discovery Science, pp.165-178, 2011.

D. Sau, L. Lee, and . De-raedt, An e cient algorithm for mining string databases under constraints, KDID 2004, Knowledge Discovery in Inductive Databases, Proceedings of the Third International Workshop on Knowledge Discovery inInductive Databases, pp.108-129, 2004.

D. Leman, A. Feelders, and A. J. Knobbe, Exceptional model mining, Machine Learning and Knowledge Discovery in Databases, pp.1-16, 2008.

F. Lemmerich, M. Rohlfs, and M. Atzmüller, Fast discovery of relevant subgroup patterns, Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, 2010.

F. Lemmerich, M. Becker, P. Singer, D. Helic, A. Hotho et al., Mining subgroups with exceptional transition behavior, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.965-974, 2016.

J. Li, H. Dani, X. Hu, and H. Liu, Radar: Residual analysis for anomaly detection in attributed networks, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp.2152-2158, 2017.

N. Li, H. Sun, K. C. Chipman, J. George, and X. Yan, A probabilistic approach to uncovering attributed graph anomalies, Proceedings of the 2014 SIAM International Conference on Data Mining, pp.82-90, 2014.

R. Li, K. H. Lei, R. Khadiwala, and K. Chang, Tedas: A twitter-based event detection and analysis system, ICDE'12, pp.1273-1276, 2012.

Y. Li, X. Kong, C. Jia, and J. Li, Clustering uncertain graphs with node attributes, Proceedings of The 10th Asian Conference on Machine Learning, pp.232-247, 2018.

J. Lij-jt, P. Papapetrou, K. Puolam, and &. Aki, A statistical significance testing approach to mining the most informative set of patterns, vol.28, p.61, 2014.

J. Lij-jt, E. Spyropoulou, B. Kang, and T. Bie, P-n-rminer: a generic framework for mining interesting structured relational patterns, I. J. Data Science and Analytics, vol.1, issue.1, pp.61-76, 2016.

B. Liu, W. Hsu, and Y. Ma, Integrating classification and association rule mining, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), pp.80-86, 1998.

G. Liu and L. Wong, E ective pruning techniques for mining quasi-cliques, Machine Learning and Knowledge Discovery in Databases, European Conference, pp.33-49, 2008.

H. Liu, Towards semantic data mining, ISWC, pp.7-11, 2010.

F. Llinares-lópez, M. Sugiyama, L. Papaxanthos, and K. M. Borgwardt, Fast and memory-e cient significant pattern mining via permutation testing, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.725-734, 2015.

K. Makino and T. Uno, New algorithms for enumerating all maximal cliques, Algorithm Theory -SWAT 2004, 9th Scandinavian Workshop on Algorithm Theory, pp.260-272, 2004.

H. Mannila and H. Toivonen, Discovering generalized episodes using minimal occurrences, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pp.146-151, 1996.

H. Mannila and H. Toivonen, Levelwise search and borders of theories in knowledge discovery, Data Min. Knowl. Discov, vol.1, issue.3, pp.241-258, 1997.

T. Hideo-matsuda, A. Ishihara, and . Hashimoto, Classifying molecular sequences using a linkage graph with their pairwise similarities, Theor. Comput. Sci, vol.210, issue.2, pp.305-325, 1999.

N. Méger and C. Rigotti, Constraint-based mining of episode rules and optimal window sizes, Knowledge Discovery in Databases: PKDD 2004, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp.313-324, 2004.

A. Benjamin, M. S. Miller, P. J. Beard, N. T. Wolfe, and . Bliss, A spectral framework for anomalous subgraph detection, IEEE Trans. Signal Processing, vol.63, issue.16, pp.4191-4206, 2015.

A. Mislove, P. K. Massimiliano-marcon, and . Gummadi, Peter Druschel, and Bobby Bhattacharjee. Measurement and analysis of online social networks, Proceedings of the 7th ACM SIGCOMM Internet Measurement Conference, pp.29-42, 2007.

S. Moens and M. Boley, Instant exceptional model mining using weighted controlled pattern sampling, Advances in Intelligent Data Analysis XIII -13th International Symposium, pp.203-214, 2014.

S. Moens and B. Goethals, Randomly sampling maximal itemsets, Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, IDEA@KDD 2013, pp.79-86, 2013.

M. Moranges, M. Plantevit, A. P. Fournel, M. Bensafi, and C. Robardet, Exceptional attributed subgraph mining to understand the olfactory percept, Discovery Science -21st International Conference, pp.276-291, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01878375

F. Moser, R. Colak, A. Rafiey, and M. Ester, Mining cohesive patterns from graphs with feature vectors, Proceedings of the SIAM International Conference on Data Mining, pp.593-604, 2009.

P. Mougel, C. Rigotti, and O. Gandrillon, Finding collections of k-clique percolated components in attributed graphs, Advances in Knowledge Discovery and Data Mining -16th Pacific-Asia Conference, PAKDD 2012, pp.181-192, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00758843

P. Mougel, C. Rigotti, M. Plantevit, and O. Gandrillon, Finding maximal homogeneous clique sets, Knowl. Inf. Syst, vol.39, issue.3, pp.579-608, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00827164

E. Müller, P. I. Sánchez, Y. Mülle, and K. Böhm, Ranking outlier nodes in subspaces of attributed graphs, Workshops Proceedings of the 29th IEEE International Conference on Data Engineering, pp.216-222, 2013.

B. Négrevergne, A. Termier, M. Rousset, and J. Méhaut, Para miner: a generic pattern mining algorithm for multi-core architectures, Data Min. Knowl. Discov, vol.28, issue.3, pp.593-633, 2014.

J. Neville, M. Adler, and D. Jensen, Clustering relational data using attribute and link information, Proceedings of the text mining and link analysis workshop, 18th international joint conference on artificial intelligence, pp.9-15, 2003.

S. Nijssen, Tree mining, Encyclopedia of Machine Learning and Data Mining, pp.1284-1292, 2017.

S. Nijssen and J. N. Kok, Frequent graph mining and its application to molecular databases, Proceedings of the IEEE International Conference on Systems, Man & Cybernetics: The Hague, pp.4571-4577, 2004.

P. K. Novak, N. Lavrac, G. R. , and I. Webb, Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining, Journal of Machine Learning Research, vol.10, pp.377-403, 2009.

V. Pachón, J. M. Vázquez, J. L. Domínguez, and M. J. Maña-lópez, Multiobjective evolutionary approach for subgroup discovery, Hybrid Artificial Intelligent Systems6th International Conference, pp.271-278, 2011.

L. Page, S. Brin, R. Motwani, and T. Winograd, The pagerank citation ranking: Bringing order to the web, WWW, pp.161-172, 1998.

S. Papadopoulos, D. Corney, and L. M. Aiello, SNOW Data Challenge, vol.1150, 2014.

J. Pei, J. Han, V. S. Laks, and . Lakshmanan, Pushing convertible constraints in frequent itemset mining, Data Min. Knowl. Discov, vol.8, issue.3, pp.227-252, 2004.

J. Pei, D. Jiang, and A. Zhang, On mining cross-graph quasi-cliques, Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.228-238, 2005.

L. Pellegrina and F. Vandin, E cient mining of the most significant patterns with permutation testing, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.2070-2079, 2018.

Z. Peng, M. Luo, J. Li, H. Liu, and Q. Zheng, ANOMALOUS: A joint modeling approach for anomaly detection on attributed networks, Proceedings of the TwentySeventh International Joint Conference on Artificial Intelligence, IJCAI 2018, pp.3513-3519, 2018.

J. A. José-alberto-pérez-melián, C. Conejero, and . Ramirez, Zipf's and benford's laws in twitter hashtags, EACL, pp.84-93, 2017.

E. Charles and . Perkins, Ad hoc networking, vol.1, 2001.

B. Perozzi and L. Akoglu, Scalable anomaly ranking of attributed neighborhoods, Proceedings of the 2016 SIAM International Conference on Data Mining, pp.207-215, 2016.

A. Prado, M. Plantevit, C. Robardet, and J. Boulicaut, Mining graph topological patterns: Finding covariations among vertex descriptors, IEEE Trans. Knowl. Data Eng, vol.25, issue.9, pp.2090-2104, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01351727

G. Qi, C. C. Aggarwal, Q. Tian, J. Heng, and T. S. Huang, Exploring context and content links in social media: A latent space method, IEEE Trans. Pattern Anal. Mach. Intell, vol.34, issue.5, pp.850-862, 2012.

M. Luc-de-raedt, S. D. Jaeger, H. Lee, and . Mannila, A theory of inductive query answering, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002, pp.123-130, 2002.

C. Robardet, Constraint-based pattern mining in dynamic graphs, The Ninth IEEE International Conference on Data Mining, pp.950-955, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01437815

D. Rodríguez, R. Ruiz, J. C. Riquelme, and J. S. Aguilar-ruiz, Searching for rules to detect defective modules: A subgroup discovery approach, Inf. Sci, vol.191, pp.14-30, 2012.

Y. Ruan, D. Fuhry, and S. Parthasarathy, E cient community detection in large networks using content and links, 22nd International World Wide Web Conference, WWW '13, pp.1089-1098, 2013.

S. Rüping, Ranking interesting subgroups, ICML, vol.28, p.27, 2009.

H. Ryang and U. Yun, Top-k high utility pattern mining with e ective threshold raising strategies, vol.76, p.27, 2015.

K. Tanay, M. A. Saha, and . Hasan, Fs 3 : A sampling based method for top-k frequent subgraph mining, Statistical Analysis and Data Mining, vol.8, issue.4, pp.245-261, 2015.

T. Sakaki, M. Okazaki, and Y. Matsuo, Earthquake shakes twitter users: real-time event detection by social sensors, WWW, vol.87, pp.851-860, 2010.

P. I. Sánchez, E. Müller, O. Irmler, and K. Böhm, Local context selection for outlier ranking in graphs with multiple numeric node attributes, Conference on Scientific and Statistical Database Management, SSDBM '14, vol.16, pp.1-16, 2014.

A. Sanfeliu and K. Fu, A distance measure between attributed relational graphs for pattern recognition, IEEE Trans. Systems, Man, and Cybernetics, vol.13, issue.3, pp.353-362, 1983.

J. Sese, M. Seki, and M. Fukuzaki, Mining networks with shared items, Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, pp.1681-1684, 2010.

J. Shang, C. Wang, C. Wang, G. Guo, and J. Qian, AGAR: an attribute-based graph refining method for community search, Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory, pp.65-66, 2016.

A. Silberschatz and A. Tuzhilin, On subjective measures of interestingness in knowledge discovery, Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), pp.275-281, 1995.

A. Silva, W. Meira, and M. J. 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.

A. Silva, W. Meira, and M. J. Zaki, Mining attribute-structure correlated patterns in large attributed graphs, PVLDB, vol.5, issue.5, pp.466-477, 2012.

A. Silva, P. Bogdanov, and A. K. Singh, Hierarchical in-network attribute compression via importance sampling, 31st IEEE International Conference on Data Engineering, pp.951-962, 2015.

D. M. Smith, Algorithm 814: Fortran 90 software for floating-point multiple precision arithmetic, gamma and related functions, ACM Trans. Math. Softw, pp.377-387, 2001.

L. M. Smith, L. Zhu, K. Lerman, and A. G. Percus, Partitioning networks with node attributes by compressing information flow, TKDD, vol.11, issue.2, 2016.

A. Soulet, Two decades of pattern mining: Principles and methods, Business Intelligence -6th European Summer School, pp.59-78, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01624606

A. Soulet, C. Raïssi, M. Plantevit, and B. Crémilleux, Mining dominant patterns in the sky, 11th IEEE International Conference on Data Mining, ICDM 2011, pp.655-664, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00623566

M. Sozio and A. Gionis, The community-search problem and how to plan a successful cocktail party, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.939-948, 2010.

R. Srikant and R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, Advances in Database Technology -EDBT'96, 5th International Conference on Extending Database Technology, pp.3-17, 1996.

A. Terada, M. Okada-hatakeyama, K. Tsuda, and J. Sese, Statistical significance of combinatorial regulations, Proceedings of the National Academy of Sciences, vol.110, issue.32, pp.12996-13001, 2013.

A. Termier, Distributed computing for enumeration, Encyclopedia of Algorithms, pp.574-577, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01178769

A. Termier, M. Rousset, and M. Sebag, DRYADE: A new approach for discovering closed frequent trees in heterogeneous tree databases, Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), pp.543-546, 2004.

A. Termier, M. Rousset, M. Sebag, K. Ohara, T. Washio et al., E cient mining of high branching factor attribute trees, Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp.785-788, 2005.

A. Termier, M. Rousset, M. Sebag, K. Ohara, T. Washio et al., Dryadeparent, an e cient and robust closed attribute tree mining algorithm

, IEEE Trans. Knowl. Data Eng, vol.20, issue.3, pp.300-320, 2008.

E. Tomita, A. Tanaka, and H. Takahashi, The worst-case time complexity for generating all maximal cliques and computational experiments, Theor. Comput. Sci, vol.363, issue.1, pp.28-42, 2006.

J. R. Ullmann, An algorithm for subgraph isomorphism, J. ACM, vol.23, issue.1, pp.31-42, 1976.

T. Uno, An e cient algorithm for solving pseudo clique enumeration problem, Algorithmica, vol.56, issue.1, pp.3-16, 2010.

T. Uno, T. Asai, Y. Uchida, and H. Arimura, LCM: an e cient algorithm for enumerating frequent closed item sets, FIMI '03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, p.19

T. Matthijs-van-leeuwen, E. De-bie, C. Spyropoulou, and . Mesnage, Subjective interestingness of subgraph patterns, Machine Learning, vol.105, pp.41-75, 2016.

A. Vavpetic and N. Lavrac, Semantic subgroup discovery systems and workflows in the sdm-toolkit, Comput. J, vol.56, issue.3, pp.304-320, 2013.

A. Vavpetic, P. K. Novak, M. Grcar, I. Mozetic, and N. Lavrac, Semantic data mining of financial news articles, Discovery Science, pp.294-307, 2013.

M. Walther and M. Kaisser, Geo-spatial event detection in the twitter stream, European Conference on Advances in IR, pp.356-367, 2013.

B. Wang, L. Cao, H. Suzuki, and K. Aihara, Epidemic spread in adaptive networks with multitype agents, Journal of Physics A: Mathematical and Theoretical, vol.44, issue.3, p.35101, 2010.

J. Wang, J. Cheng, and A. Fu, Redundancy-aware maximal cliques, KDD 2013, pp.122-130, 2013.

K. Watanabe, M. Ochi, M. Okabe, and R. Onai, Jasmine: A real-time local-event detection system based on geolocation information propagated to microblogs, CIKM, pp.2541-2544, 2011.

J. Duncan, . Watts, H. Steven, and . Strogatz, Collective dynamics of 'small-world' networks, nature, vol.393, issue.6684, p.440, 1998.

G. Rey and I. Webb, OPUS: an e cient admissible algorithm for unordered search, J. Artif. Intell. Res, vol.3, pp.431-465, 1995.

Y. Wei, L. Singh, B. Gallagher, and D. Buttler, Overlapping target event and story line detection of online newspaper articles, DSAA 2016, vol.87, pp.222-232, 2016.

J. Weng and B. Lee, Event detection in twitter, ICWSM, vol.101, p.88, 2011.

S. Wrobel, An algorithm for multi-relational discovery of subgroups, Principles of Data Mining and Knowledge Discovery, First European Symposium, PKDD '97, pp.78-87, 1997.

D. Xin, X. Shen, Q. Mei, and J. Han, Discovering interesting patterns through user's interactive feedback, KDD, pp.773-778, 2006.

Z. Xu, Y. Ke, Y. Wang, H. Cheng, and J. Cheng, GBAGC: A general bayesian framework for attributed graph clustering, TKDD, vol.9, issue.1, 2014.

X. Yan and J. Han, gspan: Graph-based substructure pattern mining, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002, pp.721-724, 2002.

X. Yan, J. Han, and R. Afshar, Clospan: Mining closed sequential patterns in large datasets, Proceedings of the Third SIAM International Conference on Data Mining, pp.166-177, 2003.

J. Yang, J. J. Mcauley, and J. Leskovec, Community detection in networks with node attributes, 2013 IEEE 13th International Conference on Data Mining, pp.1151-1156, 2013.

M. Zaki, E ciently mining frequent trees in a forest, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.71-80, 2002.

Z. Zeng, J. Wang, L. Zhou, and G. Karypis, Coherent closed quasi-clique discovery from large dense graph databases, Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.797-802, 2006.

C. Zhang, G. Zhou, Q. Yuan, H. Zhuang, Y. Zheng et al., Geoburst: Real-time local event detection in geo-tagged tweet streams, ACM SIGIR, pp.513-522, 2016.

C. Zhang, L. Liu, D. Lei, Q. Yuan, H. Zhuang et al., Triovecevent: Embedding-based online local event detection in geo-tagged tweet streams, ACM SIGKDD, pp.595-604, 2017.

F. Zhang, Y. Zhang, L. Qin, W. Zhang, and X. Lin, When engagement meets similarity: E cient (k, r)-core computation on social networks, vol.10, pp.998-1009, 2017.

Y. Zhang, E. Levina, and J. Zhu, Community detection in networks with node features

. Corr, , 2015.

Y. Zhou, H. Cheng, and J. Yu, Graph clustering based on structural/attribute similarities, PVLDB, vol.2, issue.1, pp.718-729, 2009.

Y. Zhu, C. Yeh, Z. Zimmerman, K. Kamgar, and E. J. Keogh, Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds, IEEE International Conference on Data Mining, ICDM 2018, pp.837-846, 2018.

Z. Zou, Polynomial-time algorithm for finding densest subgraphs in uncertain graphs, Proceedings of MLG Workshop, 2013.

Z. Zou, J. Li, H. Gao, and S. Zhang, Frequent subgraph pattern mining on uncertain graph data, Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp.583-592, 2009.

Z. Zou, J. Li, H. Gao, and S. Zhang, Mining frequent subgraph patterns from uncertain graph data, IEEE Trans. Knowl. Data Eng, vol.22, issue.9, pp.1203-1218, 2010.

A. Folio and . Lyon-nom,

. Prénoms, Ahmed Anes TITRE : Mining useful patterns in attributed graphs NATURE : Doctorat Numéro, pp.2019-058

, Dans un réseau social, chaque personne peut être décrite par son âge, ses centres d'intérêts, etc. C'est ce qu'on appelle un graphe attribué. L'analyse de ce type de graphes peut offrir une grande opportunité pour extraire des informations utiles et actionnables. Cela permet d'identifier des communautés ayant des centres d'intérêts particuliers dans un réseau social, de détecter des évènements à partir des tweets partagés, etc. Dans cette thèse, nous adressons le problème de fouille de graphes attribués. Plus précisément, nous proposons des méthodes qui analysent un graphe pour identifier des motifs : des sous-graphes ayant des caractéristiques particulières. Bien que ce problème a intéressé un grand nombre de chercheurs depuis des années, il reste encore plusieurs défis à relever. Nous adressons les questions : quand est-ce qu'un motif est intéressant pour l'utilisateur ? plusieurs facteurs entrent en jeu. Nous considérons qu'un motif est intéressant : (1) s'il montre une exceptionnalité par rapport au reste du graphe, Ecole doctorale : InfoMaths (ED 512) Spécialité : Informatique RESUME : Un graphe est une structure qui permet de modéliser efficacement une large variété de données. Par exemple, un réseau social peut être représenté avec un graphe où les personnes sont les sommets, et leurs liens d'amitiés sont les arêtes

. Mots-clés, Fouille de graphes attribués, fouille de données centrée sur l'utilisateur, fouille de modèles exceptionnels, analyse des dynamiques urbaines, détection d'évènements sur les réseaux sociaux

, Laboratoire (s) de recherche : Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS)

. Directeur-de-thèse,

, Dr. Siegfried Nijssen