Online Subgraph Skyline Analysis over Knowledge Graphs

Weiguo Zheng, Xiang Lian, Lei Zou, Liang Hong, Dongyan Zhao

Abstract: Subgraph search is very useful in many real-world applications. However, users may be overwhelmed by the masses of matches. In this paper, we propose a subgraph skyline analysis problem, denoted as S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A, to support more complicated analysis over graph data. Specifically, given a large graph G and a query graph q, we want to find all the subgraphs g in G, such that g is graph isomorphic to q and not dominated by any other subgraphs. In order to improve the efficiency, we devise a hybrid feature encoding incorporating both structural and numeric features based on a partitioning strategy, and discuss how to optimize the space partitioning. We also present a skylayer index to facilitate the dynamic subgraph skyline computation. Moreover, an attribute cluster-based method is proposed to deal with the curse of dimensionality. Extensive experiments over real datasets confirm the effectiveness and efficiency of our algorithm.