Nearly Linear Time Algorithm for Mean Hitting Times of Random Walks on a Graph

For random walks on a graph, the mean hitting time $H_j$ from a vertex i chosen from the stationary distribution to the target vertex j can be used as a measure of importance for vertex j, while the Kemeny constant K is the mean hitting time from a vertex i to a vertex j selected randomly according to the stationary distribution. Both quantities have found a large variety of applications in different areas. However, their high computational complexity limits their applications, especially for large networks with millions of vertices. In this paper, we first establish a connection between the two quantities, representing K in terms of $H_j$ for all vertices. We then express both quantities in terms of quadratic forms of the pseudoinverse for graph Laplacian, based on which we develop an efficient algorithm that provides an approximation of $H_j$ for all vertices and K in nearly linear time with respect to the edge number, with high probability. Extensive experiment results on real-life and model networks validate both the efficiency and accuracy of the proposed algorithm. Download paper here

Recommended citation: Zhang Zuobai, Xu Wanyue, and Zhang Zhongzhi. Nearly linear time algorithm for mean hitting times of random walks on a graph. In: Proceedings of ACM International Conference on Web Search and Data Mining (WSDM 2020), Houston, USA, February 3-7, 2020, pp.726-734.