A Graph Matching Approach Based on Aggregated Search

Abstract

Graph datasets are commonly used in a broad range of application domains, which makes the graph querying a crucial task in order to fully exploit the knowledge within these datasets. Using approximate graph matching tools is usually preferred due to the noisy nature of graph datasets, with the aim of overcoming restrictive query answering. In this paper, we propose a novel approach for approximate graph matching using aggregated search, called Label and Structure Similarity Aggregated Search (LaSaS). LaSaS yields effective and efficient graph querying without having a fixed schema of the data graph through: (i) the use of the aggregated search strategy to raise the number of answers, (ii) the use of a new low-cost graph similarity metric that considers similarities of nodes labels and graphs structures to enable finding approximate matches.