An efficient shortest path approach for social networks based on community structure

Finding the shortest path (SP) in a large-scale network analysis between any two nodes is a tough but very significant task. The SP can help us to analyze the information spreading performance and research the latent relationship in the weighted social network, and so on. As the size of the social network increases, the traditional SP algorithms have poor performance and there is not a suitable algorithm for weighted social network. Some features of the network analysis are beneficial to solve this problem, and community structure ignored by the traditional methods is one of the most important features. In this paper, we propose a shortest path algorithm based on community detection (SPCD) by integrating community detection algorithm with traditional search methods. SPCD constructs a community graph by using community structure to narrow the searching scope. The algorithm presented improves the time efficiency and maintains the accuracy scale of the SP. Experimental results on five real-world networks demonstrate the effectiveness of the proposed methods for the SP problem.