Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Morphing: combining structure and randomness
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Modelling More Realistic SAT Problems
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Partition-based logical reasoning for first-order and propositional theories
Artificial Intelligence - Special volume on reformulation
Predicting learnt clauses quality in modern SAT solvers
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
On the structure of industrial SAT instances
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Problem structure in the presence of perturbations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Eigenvector centrality in industrial SAT instances
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Community-Based partitioning for MaxSAT solving
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
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The research community on complex networks has developed techniques of analysis and algorithms that can be used by the SAT community to improve our knowledge about the structure of industrial SAT instances. It is often argued that modern SAT solvers are able to exploit this hidden structure, without a precise definition of this notion. In this paper, we show that most industrial SAT instances have a high modularity that is not present in random instances. We also show that successful techniques, like learning, (indirectly) take into account this community structure. Our experimental study reveal that most learnt clauses are local on one of those modules or communities.