R. Agrawal and R. Skirant, Fast algorithms for mining association rules, proceedings of the 20 th International Conference on Very Large Databases, pp.478-499, 1994.

C. Bessì-ere and J. Régin, Mac and combined heuristics: Two reasons to forsake fc (and cbj?) on hard problems, Lecture Notes in Computer Science, vol.1118, pp.61-75, 1996.

D. J. Cook and L. B. Holder, Mining Graph Data, 2006.

B. Douar, M. Liquiere, C. Latiri, and Y. Slimani, FGMAC: Frequent subgraph mining with Arc Consistency, Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, pp.112-119, 2011.
URL : https://hal.archives-ouvertes.fr/lirmm-00757478

M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, 1979.

P. Hell and J. Nesetril, Graphs and homomorphism, vol.28, 2004.

C. Helma, R. D. King, S. Kramer, and A. Srinivasan, The predictive toxicology challenge, Bioinformatics, vol.17, issue.1, pp.107-108, 2001.

J. Huan, W. Wang, and J. Prins, Efficient mining of frequent subgraphs in the presence of isomorphism, International Conference on Data Mining, p.549, 2003.

A. Inokuchi, T. Washio, and H. Motoda, Complete mining of frequent patterns from graphs: Mining graph data, Machine Learning, vol.50, pp.321-354, 2003.

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

M. Kuramochi and G. Karypis, An efficient algorithm for discovering frequent subgraphs, IEEE Transactions on Knowledge and Data Engineering, vol.16, pp.1038-1051, 2004.

M. Liquiere, Arc consistency projection: A new generalization relation for graphs, LNCS, vol.4604, pp.333-346, 2007.
URL : https://hal.archives-ouvertes.fr/lirmm-00196399

A. K. Mackworth, Consistency in networks of relations, Artif. Intell, vol.8, issue.1, pp.99-118, 1977.

S. Nijssen and J. N. Kok, The gaston tool for frequent subgraph mining, InterMarch, vol.9, 2014.

B. Douar, national Workshop on Graph-Based Tools (Grabats), pp.77-87, 2004.

J. R. Quinlan, C4.5: Programs for Machine Learning, 1993.

M. D. Wessel, P. C. Jurs, J. W. Tolan, and S. M. Muskal, Prediction of human intestinal absorption of drug compounds from molecular structure, Journal of Chemical Information and Computer Sciences, vol.38, issue.4, pp.726-735, 1998.

I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques

M. Wörlein, T. Meinl, I. Fischer, and M. Philippsen, A quantitative comparison of the subgraph miners mofa, gspan, ffsm, and gaston, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, vol.3721, pp.392-403, 2005.

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