Accelerating Maximal Clique Enumeration via Graph Reduction

TL;DR

This paper introduces RMCE, a reduction-based framework that significantly accelerates maximal clique enumeration by reducing the search space and redundant computations, achieving up to 44.7x speedup.

Contribution

The paper presents a novel reduction framework with three techniques that improve the efficiency of maximal clique enumeration over existing recursive methods.

Findings

Achieves up to 44.7x speedup on real graphs.

Reduces search space and computation through three reduction techniques.

Demonstrates effectiveness on 18 real-world datasets.

Abstract

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.