An Optimized Lossless Graph Summarization for Large-Scale Graphs.

Graphs have been widely used for modeling large-scale data generated from real-world applications, while compact representation of such graphs is beneficial for efficient storage and effective graph analysis. As a promising solution, lossless graph summarization can compactly represent a given graph as a summary graph, which consists of supernodes (i.e., sets of nodes) and superedges (edges between supernodes), and the correction edge sets, which together with summary graph can exactly reconstruct the original graph. Although many research efforts have been devoted to develop graph summarization methods, existing works are still inefficient in terms of computation efficiency and representation compactness. To address their limitations, we propose optGS that includes a set of optimization techniques, including computation-oriented supernode re-dividing, degree-aware approximation metric for selecting the best merge, and redundant computation avoidance, to improve current advances. Extensive experiments on a variety of large graph datasets demonstrate the computation efficiency and compression effectiveness of our optGS, e.g., improving the representation compactness by up to 20.28% and achieving 3.53× speedup in running time than the state-of-the-art methods.