R. Ahmed and G. Karypis, Algorithms for Mining the Evolution of Conserved Relational States in Dynamic Networks, ICDM, pp.1-10, 2011.

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

M. Atzmüller and F. Puppe, SD-Map ??? A Fast Algorithm for Exhaustive Subgroup Discovery, PKDD, pp.6-17, 2006.
DOI : 10.1007/11871637_6

M. Berlingerio, F. Bonchi, B. Bringmann, and A. Gionis, Mining Graph Evolution Rules, ECML/PKDD, pp.115-130, 2009.
DOI : 10.1007/978-3-540-71701-0_38
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.184.2851

M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, and D. Pedreschi, Multidimensional networks: foundations of structural analysis, World Wide Web, vol.11, issue.2, pp.5-6567, 2013.
DOI : 10.1007/s11280-012-0190-4

J. Besson, C. Robardet, and J. Boulicaut, Mining a new faulttolerant pattern type as an alternative to formal concept discovery, Conceptual Structures: Inspiration and Application, 14th International Conference on Conceptual Structures Proceedings, volume 4068 of Lecture Notes in Computer Science, pp.144-157, 2006.
DOI : 10.1007/11787181_11

B. Boden, S. Günnemann, H. Hoffmann, and T. Seidl, Mining coherent subgraphs in multi-layer graphs with edge labels, KDD, pp.1258-1266, 2012.
DOI : 10.1145/2339530.2339726

F. Bonchi, A. Gionis, F. Gullo, and A. Ukkonen, Chromatic correlation clustering, KDD, pp.1321-1329, 2012.
DOI : 10.1145/2728170
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.417

K. M. Borgwardt, H. Kriegel, and P. Wackersreuther, Pattern Mining in Frequent Dynamic Subgraphs, Sixth International Conference on Data Mining (ICDM'06), pp.818-822, 2006.
DOI : 10.1109/ICDM.2006.124
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.5252

M. Björn-bringmann, F. Berlingerio, A. Bonchi, and . Gionis, Learning and Predicting the Evolution of Social Networks, IEEE Intelligent Systems, vol.25, issue.4, pp.26-35, 2010.
DOI : 10.1109/MIS.2010.91

M. Das, S. Amer-yahia, G. Das, and C. Yu, MRI: Meaningful interpretations of collaborative ratings, pp.1063-1074, 2011.

M. Das, S. Thirumuruganathan, S. Amer-yahia, G. Das, and C. Yu, An expressive framework and efficient algorithms for the analysis of collaborative tagging, The VLDB Journal, vol.37, issue.9, pp.201-226, 2014.
DOI : 10.1007/s00778-013-0341-y

P. Olmo-vaz-de-melo, C. Faloutsos, and A. Loureiro, Human dynamics in large communication networks, SDM, pp.968-879, 2011.

E. Desmier, M. Plantevit, C. Robardet, and J. Boulicaut, Trend Mining in Dynamic Attributed Graphs, ECML/PKDD, pp.654-669, 2013.
DOI : 10.1007/978-3-642-40988-2_42
URL : https://hal.archives-ouvertes.fr/hal-01339225

W. Duivesteijn, A short survey of exceptional model mining: Exploring unusual interactions between multiple targets, 2014 International Workshop on Multi-Target Prediction, 2014.

W. Duivesteijn, A. Feelders, and A. J. Knobbe, Exceptional Model Mining, Data Mining and Knowledge Discovery, vol.77, issue.1, pp.47-98, 2016.
DOI : 10.1007/s10618-015-0403-4
URL : https://openaccess.leidenuniv.nl/bitstream/handle/1887/21760/dissertation.pdf?sequence=22

W. Duivesteijn, A. J. Knobbe, A. Feelders, and M. Van-leeuwen, Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach, 2010 IEEE International Conference on Data Mining, pp.14-17, 2010.
DOI : 10.1109/ICDM.2010.53

M. Girvan, E. Mark, and . Newman, Community structure in social and biological networks, Proceedings of the National Academy of Sciences, pp.7821-7826, 2002.
DOI : 10.1103/PhysRevLett.85.4633
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC122977

A. Goyal, F. Bonchi, V. S. Laks, S. Lakshmanan, and . Venkatasubramanian, On minimizing budget and time in influence propagation over social networks, Social Network Analysis and Mining, vol.2, issue.4, pp.179-192, 2013.
DOI : 10.1007/s13278-012-0062-z

S. Günnemann, I. Färber, B. Boden, and T. Seidl, Subspace clustering meets dense subgraph mining, ICDM, pp.845-850, 2010.

R. Hamon, Analysis of temporal networks using signal processing methods : Application to the bike-sharing system in Lyon, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01216173

A. Inokuchi and T. Washio, FRISSMiner: Mining Frequent Graph Sequence Patterns Induced by Vertices, SDM, pp.466-477, 2010.
DOI : 10.1587/transinf.E95.D.1590

M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang et al., Social contextual recommendation, Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM '12, pp.45-54, 2012.
DOI : 10.1145/2396761.2396771

M. Kaytoue, Y. Pitarch, M. Plantevit, and C. Robardet, Triggering patterns of topology changes in dynamic graphs, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp.158-165, 2014.
DOI : 10.1109/ASONAM.2014.6921577
URL : https://hal.archives-ouvertes.fr/hal-01301075

M. Kaytoue, A. Silva, L. Cerf, W. M. Jr, and C. Ra¨?ssira¨?ssi, Watch me playing, i am a professional, Proceedings of the 21st international conference companion on World Wide Web, WWW '12 Companion, pp.1181-1188, 2012.
DOI : 10.1145/2187980.2188259
URL : https://hal.archives-ouvertes.fr/hal-00697150

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

M. Lahiri and T. Y. Berger-wolf, Mining Periodic Behavior in Dynamic Social Networks, 2008 Eighth IEEE International Conference on Data Mining, pp.373-382, 2008.
DOI : 10.1109/ICDM.2008.104

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.

D. Leman, A. Feelders, and A. J. Knobbe, Exceptional Model Mining, ECML/PKDD, pp.1-16, 2008.
DOI : 10.1007/978-3-540-87481-2_1

F. Lemmerich, M. Becker, and M. Atzmueller, Generic Pattern Trees for Exhaustive Exceptional Model Mining, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2012 Proceedings, Part II, pp.277-292, 2012.
DOI : 10.1007/978-3-642-33486-3_18
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.400.8956

S. C. Madeira and A. L. Oliveira, Biclustering algorithms for biological data analysis: a survey, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.1, issue.1, pp.24-45, 2004.
DOI : 10.1109/TCBB.2004.2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.1289

T. M. , M. Schubert, and A. Drachen, Esports analytics through encounter detection, Proceedings of the MIT Sloan Sports Analytics Conference 2016, 2016.

S. Morishita and J. Sese, Traversing itemset lattice with statistical metric pruning, PODS, 2000.
DOI : 10.1145/335168.335226

F. Moser, R. Colak, A. Rafiey, and M. Ester, Mining Cohesive Patterns from Graphs with Feature Vectors, SDM, pp.593-604, 2009.
DOI : 10.1137/1.9781611972795.51
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.2030

P. Mougel, C. Rigotti, M. Plantevit, and O. Gandrillon, Finding maximal homogeneous clique sets, Knowledge and Information Systems, vol.2, issue.1, pp.1-30, 2013.
DOI : 10.1007/s10115-013-0625-y
URL : https://hal.archives-ouvertes.fr/hal-00827164

P. Kralj-novak, N. Lavra?, and G. I. Webb, Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining, J. Mach. Learn. Res, vol.10, pp.377-403, 2009.

S. Ontañón, G. Synnaeve, A. Uriarte, F. Richoux, D. Churchill et al., A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft, IEEE Transactions on Computational Intelligence and AI in Games, vol.5, issue.4, pp.293-311, 2013.
DOI : 10.1109/TCIAIG.2013.2286295

K. Pearson, On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, issue.302, pp.50157-175, 1900.

A. Prado, B. Jeudy, E. Fromont, and F. Diot, Mining spatiotemporal patterns in dynamic plane graphs, Intell. Data Anal, vol.17, issue.1, pp.71-92, 2013.
URL : https://hal.archives-ouvertes.fr/ujm-00629121

A. Prado, M. Plantevit, C. Robardet, and J. Boulicaut, Mining Graph Topological Patterns: Finding Covariations among Vertex Descriptors, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.9, 2013.
DOI : 10.1109/TKDE.2012.154
URL : https://hal.archives-ouvertes.fr/hal-01351727

C. C. Guo-jun-qi, Q. Aggarwal, H. Tian, T. S. Ji, and . Huang, Exploring Context and Content Links in Social Media: A Latent Space Method, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.5, pp.850-862, 2012.
DOI : 10.1109/TPAMI.2011.191

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

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. Silva, W. Meira, and M. J. Zaki, Mining attribute-structure correlated patterns in large attributed graphs, Proceedings of the VLDB Endowment, vol.5, issue.5, pp.466-477, 2012.
DOI : 10.14778/2140436.2140443
URL : http://arxiv.org/abs/1201.6568

A. Soulet, C. Ra¨?ssira¨?ssi, M. Plantevit, and B. Crémilleux, Mining Dominant Patterns in the Sky, 2011 IEEE 11th International Conference on Data Mining, pp.655-664, 2011.
DOI : 10.1109/ICDM.2011.100
URL : https://hal.archives-ouvertes.fr/inria-00623566

Y. Sun and J. Han, Mining Heterogeneous Information Networks: Principles and Methodologies, Synthesis Lectures on Data Mining and Knowledge Discovery, vol.3, issue.2, 2012.
DOI : 10.2200/S00433ED1V01Y201207DMK005

T. L. Taylor, Raising the Stakes:E-Sports and the Professionalization of Computer Gaming, 2012.

H. Tong, S. Papadimitriou, J. Sun, P. S. Yu, and C. Faloutsos, Colibri, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.686-694, 2008.
DOI : 10.1145/1401890.1401973

. Matthijs-van-leeuwen, Maximal exceptions with minimal descriptions, Data Mining and Knowledge Discovery, vol.177, issue.1, pp.259-276, 2010.
DOI : 10.1007/s10618-010-0187-5

M. Van-leeuwen and A. J. Knobbe, Diverse subgroup set discovery, Data Mining and Knowledge Discovery, vol.3, issue.1, pp.208-242, 2012.
DOI : 10.1007/s10618-012-0273-y

A. Von-eschen, Machine learning and data mining in call of duty

Y. Yang, J. Yu, H. Gao, J. Pei, and J. Li, Mining most frequently changing component in evolving graphs, World Wide Web, vol.14, issue.5&6, pp.1-26, 2013.
DOI : 10.1007/s11280-013-0204-x

L. B. Chang-hun-you, D. J. Holder, and . Cook, Learning patterns in the dynamics of biological networks, KDD, pp.977-986, 2009.