G. Sadowski and P. Rathle, Fraud detection: Discovering connections with graph databases, in: White Paper -Neo Technology -Graphs are Every- 630 where, 2014.

J. A. Bondy, Graph Theory With Applications, 1976.
DOI : 10.1007/978-1-349-03521-2

Z. Sun, B. Shao, H. Wang, J. Li, and H. Wang, Efficient subgraph matching on billion node graphs, PVLDB, p.640, 2012.
DOI : 10.14778/2311906.2311907

R. Angles and C. Gutiérrez, Survey of graph database models, ACM Computing Surveys, vol.40, issue.1
DOI : 10.1145/1322432.1322433

A. Castelltort and A. Laurent, Representing history in graph-oriented NoSQL databases: A versioning system, Eighth International Conference on Digital Information Management (ICDIM 2013), 2013.
DOI : 10.1109/ICDIM.2013.6694022

URL : https://hal.archives-ouvertes.fr/lirmm-01381081

A. Kosmatopoulos, K. Giannakopoulou, A. N. Papadopoulos, and K. Tsichlas, An Overview of Methods for Handling Evolving Graph Sequences, Revised Selected Papers Lecture Notes in Computer Science, vol.9511, pp.181-192, 2015.
DOI : 10.1007/978-3-319-29919-8_14

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3
DOI : 10.1145/1541880.1541882

C. C. Noble and D. J. Cook, Graph-based anomaly detection, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.631-636, 2003.
DOI : 10.1145/956750.956831

]. C. Jedrzejek, J. Bak, and M. Falkowski, Graph mining for detection of a large class of financial crimes, 17th International Conference on Conceptural structure, p.660, 2009.

K. Bastani, Bank Fraud Detection, 2014.

A. Leontjeva, K. Tretyakov, J. Vilo, and T. Tamkivi, Fraud Detection: Methods of Analysis for Hypergraph Data, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp.1060-1064, 2012.
DOI : 10.1109/ASONAM.2012.234

T. Horvth, T. Grtner, and S. Wrobel, Cyclic pattern kernels for predictive graph mining, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.158-167, 2004.
DOI : 10.1145/1014052.1014072

A. Castelltort and A. Laurent, Fuzzy Queries over NoSQL Graph Databases: Perspectives for Extending the Cypher Language, International Conference on Processing and Management of Uncertainty in Knowledge-Based Sys- 680 tems, 2014.
DOI : 10.1007/978-3-319-08852-5_40

URL : https://hal.archives-ouvertes.fr/lirmm-01381080

O. Pivert, O. Slama, G. Smits, and V. Thion, SUGAR: A graph database fuzzy querying system, 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS), 2016.
DOI : 10.1109/RCIS.2016.7549366

URL : https://hal.archives-ouvertes.fr/hal-01297185

O. Pivert, V. Thion, H. Jaudoin, and G. Smits, On a Fuzzy Algebra for Querying Graph Databases, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, pp.748-755, 2014.
DOI : 10.1109/ICTAI.2014.116

URL : https://hal.archives-ouvertes.fr/hal-01059991

P. Wadler, How to replace failure by a list of successes a method for ex- 695 ception handling, backtracking, and pattern matching in lazy functional languages, in: Functional Programming Languages and Computer Architecture, pp.113-128, 1985.

G. Hutton, Abstract, Journal of Functional Programming, vol.274, issue.03, pp.323-343, 1992.
DOI : 10.1145/365230.365257

J. Fokker, Functional parsers, pp.1-23, 1995.
DOI : 10.1007/3-540-59451-5_1

Y. Kou, C. Lu, S. Sirwongwattana, Y. Huang, P. Dua et al., Survey of fraud detection techniques, in: Networking, Sensing and Control Supervised learning methods for fraud detection in healthcare insurance, IEEE International Conference on Machine Learning in Healthcare Informatics, pp.749-754, 2004.

L. Cao, Q. Wang, and E. A. Rundensteiner, Interactive outlier exploration in big data streams, Proc. VLDB Endow
DOI : 10.14778/2733004.2733045

J. Kingdon, INTELLIGENT SYSTEMS FOR FRAUD DETECTION, Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives, Advanced Series in Electrical and Computer Engineering, pp.133-154, 1997.
DOI : 10.1142/9789814261296_0008

M. Lenard and P. Alam, Application of fuzzy logic to fraud detection, Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications, pp.135-139, 2007.

C. Yu and S. Lin, Fuzzy rule optimization for online auction frauds detection based on genetic algorithm, Electronic Commerce Research, vol.22, issue.4, pp.169-182, 2013.
DOI : 10.1007/s10660-013-9113-4

C. C. Aggarwal, Y. Zhao, and P. S. Yu, Outlier detection in graph streams, 2011 IEEE 27th International Conference on Data Engineering, pp.399-409, 2011.
DOI : 10.1109/ICDE.2011.5767885

M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, Outlier Detection for Temporal Data: A Survey, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.9, pp.2250-2267, 2014.
DOI : 10.1109/TKDE.2013.184

X. Yan and J. Han, gspan: Graph-based substructure pattern mining, Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM '02, p.721, 2002.

M. Kuramochi and G. Karypis, Frequent subgraph discovery, Proceedings 2001 IEEE International Conference on Data Mining, pp.313-320, 2001.
DOI : 10.1109/ICDM.2001.989534

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

J. Wang, K. Zhang, and G. Chirn, The approximate graph matching problem, Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), p.745
DOI : 10.1109/ICPR.1994.576921

S. S. Chawathe, S. Abiteboul, and J. Widom, Representing and querying changes in semistructured data, Proceedings 14th International Conference on Data Engineering, 1998.
DOI : 10.1109/ICDE.1998.655752

K. Y. Lee, Y. D. Chung, and M. H. Kim, An efficient method for maintaining 750 data cubes incrementally, Inf. Sci, vol.180, issue.6

S. Auer and H. Herre, A Versioning and Evolution Framework for RDF Knowledge Bases, pp.55-69, 2007.
DOI : 10.1007/978-3-540-70881-0_8

V. Papavasileiou, G. Flouris, I. Fundulaki, D. Kotzinos, and V. Christophides, High-level change detection in RDF(S) KBs, ACM Transactions on Database Systems, vol.38, issue.1, pp.1-1, 2013.
DOI : 10.1145/2445583.2445584

U. Khurana and A. Deshpande, Efficient snapshot retrieval over historical graph data, 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp.765-997, 2013.
DOI : 10.1109/ICDE.2013.6544892

URL : http://arxiv.org/abs/1207.5777

P. J. Andrei-krokhin and P. Jonsson, Reasoning about temporal relations, p.34

S. Schockaert, M. D. Cock, and E. E. Kerre, Fuzzifying Allen's Temporal Interval Relations, IEEE Transactions on Fuzzy Systems, vol.16, issue.2, pp.517-533, 2008.
DOI : 10.1109/TFUZZ.2007.895960

P. T. Wood, Query languages for graph databases, ACM SIGMOD Record, vol.41, issue.1
DOI : 10.1145/2206869.2206879

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

R. Angles, P. Barcel, and G. Ros, A practical query language for graph dbs, 7th Alberto Mendelzon International Workshop on Foundations of Data Management (AMW), 2013.

H. He and A. K. Singh, Graphs-at-a-time, Proceedings of the 2008 ACM SIGMOD international conference on Management of data , SIGMOD '08, pp.405-418, 2008.
DOI : 10.1145/1376616.1376660

A. Castelltort and A. Laurent, Fuzzy historical graph pattern matching a nosql 785 graph database approach for fraud ring resolution, Artificial Intelligence Applications and Innovations, pp.151-167, 2015.