%0 Conference Paper %F Oral %T Combining Efficient Preprocessing and Incremental MaxSAT Reasoning for MaxClique in Large Graphs %+ University of Colorado [Colorado Springs] (UCCS) %+ COntraintes, ALgorithmes et Applications (COALA) %+ Modélisation, Information et Systèmes - UR UPJV 4290 (MIS) %+ Artificial Intelligence Research Institute / Spanish Scientific Research Council (IIIA / CSIC) %A Jiang, Hua %A Li, Chu-Min %A Manya, Felip %Z 22nd European Conference on Artificial Intelligence (ECAI), Hague, NETHERLANDS, AUG 29-SEP 02, 2016 %< avec comité de lecture %B ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE %C The Hague, Netherlands %Y Kaminka %Y GA and Fox %Y M and Bouquet %Y P and Hullermeier %Y E and Dignum %Y V and Dignum %Y F and VanHarmelen %Y F %S Frontiers in Artificial Intelligence and Applications %V 285 %P 939-947 %8 2016-08-29 %D 2016 %R 10.3233/978-1-61499-672-9-939 %Z Computer Science [cs]Conference papers %X We describe a new exact algorithm for MaxClique, called LMC (short for Large MaxClique), that is especially suited for large sparse graphs. LMC is competitive because it combines an efficient preprocessing procedure and incremental MaxSAT reasoning in a branch-and-bound scheme. The empirical results show that LMC outperforms existing exact MaxClique algorithms on large sparse graphs from real-world applications. %G English %L hal-03636430 %U https://u-picardie.hal.science/hal-03636430 %~ UNIV-TLN %~ CNRS %~ UNIV-AMU %~ UNIV-PICARDIE %~ LIS-LAB %~ U-PICARDIE %~ MIS