PROJECT TITLE:

Online Subgraph Skyline Analysis Over Knowledge Graphs - 2016

ABSTRACT:

Subgraph search is terribly useful in several real-world applications. However, users might be overwhelmed by the lots of matches. In this paper, we have a tendency to propose a subgraph skyline analysis drawback, denoted as S2A, to support more sophisticated analysis over graph information. Specifically, given a massive graph G and a query graph q, we tend to want to seek out all the subgraphs g in G, such that g is graph isomorphic to q and not dominated by any different subgraphs. So as to improve the potency, we devise a hybrid feature encoding incorporating each structural and numeric features based on a partitioning strategy, and discuss a way to optimize the area partitioning. We tend to additionally gift a skylayer index to facilitate the dynamic subgraph skyline computation. Moreover, an attribute cluster-primarily based methodology is proposed to deal with the curse of dimensionality. In depth experiments over real datasets make sure the effectiveness and efficiency of our algorithm.