  As a fundamental task in graph data management, maximal clique enumeration
(MCE) has attracted extensive attention from both academic and industrial
communities due to its wide range of applications. However, MCE is very
challenging as the number of maximal cliques may grow exponentially with the
number of vertices. The state-of-the-art methods adopt a recursive paradigm to
enumerate maximal cliques exhaustively, suffering from a large amount of
redundant computation. In this paper, we propose a novel reduction-based
framework for MCE, namely RMCE, that aims to reduce the search space and
minimize unnecessary computations. The proposed framework RMCE incorporates
three kinds of powerful reduction techniques including global reduction,
dynamic reduction, and maximality check reduction. Global and dynamic reduction
techniques effectively reduce the size of the input graph and dynamically
construct subgraphs during the recursive subtasks, respectively. The maximality
check reduction minimizes the computation for ensuring maximality by utilizing
neighborhood dominance between visited vertices. Extensive experiments on 18
real graphs demonstrate the effectiveness of our proposed method. It achieves
remarkable speedups up to 44.7x compared to existing approaches.
