Efficient Parallel Computing of Graph Edit Distance

Abstract

With the prevalence of graph data, graph edit distance (GED), a well-known measure of similarity between two graphs, has been widely used in many real applications, such as graph classification and clustering, similar object detection, and biology network analysis. Despite its usefulness and popularity, GED is computationally costly, because it is NP-hard. Currently, most existing solutions focus on computing GED in a serial manner and little attention has been paid for parallel computing. In this paper, we propose a novel efficient parallel algorithm for computing GED. Our algorithm is based on the state[1]of-the-art GED algorithm AStar+-LSa, and is called PGED. The main idea of PGED is to allocate the heavy workload of searching the optimal vertex mapping between two graphs, which is the most time consuming step, to multiple threads based on an effective allocation strategy, resulting in high efficiency of GED computation. We have evaluated PGED on two real datasets, and the experimental results show that by using multiple threads, PGED is more efficient than AStar+-LSa. In addition, by carefully tuning the parameters, the performance of PGED can be further improved.