When working with a network, it is often of interest to locate the “most important” nodes in the network. A common way to do this is by using some graph centrality measures. In this chapter, the authors focus on the centrality measures based on powers of the adjacency matrix and those based on random walk. They introduce the concept of linear systems and the Neumann series. The authors give a few derivations coupled with a summary of the power series expressions for the centrality measures. The generalization of the M-matrix for monotonic functions is well described, with an emphasis on lazy variants of alpha, Katz and cumulative nomination centrality measures, their convergence conditions, relations and a summary of their mathematical formulations. The authors implement the experimental results by the use of heat maps.