Mean Hitting Time for Random Walks on a Class of Sparse Networks - Jing Su; Xiaomin Wang; Bing Yao (Entropy, 2021)
For random walks on a complex network, the configuration of a network that provides optimal or suboptimal navigation efficiency is meaningful research. It has been proven that a complete graph has the exact minimal mean hitting time, which grows linearly with the network order. In this paper, we present a class of sparse networks <semantics>G(t)</semantics> in view of a graphic operation, which have a similar dynamic process with the complete graph; however, their topological properties are different. We capture that <semantics>G(t)</semantics> has a remarkable scale-free nature that exists in most real networks and give the recursive relations of several related matrices for the studied network. According to the connections between random walks and electrical networks, three types of graph invariants are calculated, including regular Kirchhoff index, M-Kirchhoff index and A-Kirchhoff index. We derive the closed-form solutions for the mean hitting time of <semantics>G(t)</semantics>, and our results show that the dominant scaling of which exhibits the same behavior as that of a complete graph. The result could be considered when designing networks with high navigation efficiency.