Navigating a Shortest Path with High Probability in Massive Complex Networks

In this paper, we study the problem of point-to-point shortest path query in massive complex networks. Nowadays a breadth first search in a network containing millions of vertices may cost a few seconds and it can not meet the demands of real-time applications. Some existing landmark-based methods have been proposed to solve this problem in sacrifice of precision. However, their query precision and efficiency is not high enough. We first present a notion of navigator, which is a data structure constructed from the input network. Then navigation algorithm based on the navigator is proposed to solve this problem. It effectively navigates a path only using local information of each vertex by interacting with navigator. We conduct extensive experiments in massive real-world networks containing hundreds of millions of vertices. The results demonstrate the efficiency of our methods. Compared with previous methods, ours can navigate a shortest path with higher probability in less time.

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