Fast Graph Similarity Search via Locality Sensitive Hashing

Boyu Zhang, Xianglong Liu, Bo Lang

Abstract: Similarity search in graph databases has been widely studied in graph query processing in recent years. With the fast accumulation of graph databases, it is worthwhile to develop a fast algorithm to support similarity search in large-scale graph databases. In this paper, we study k-NN similarity search problem via locality sensitive hashing. We propose a fast graph search algorithm, which first transforms complex graphs into vectorial representations based on the prototypes in the database and then accelerates query efficiency in Euclidean space by employing locality sensitive hashing. Additionally, a general retrieval framework is established in our approach. Experiments on three real datasets show that our work achieves high performance both on the accuracy and the efficiency of the presented algorithm.