This document presents an algorithm for calculating conditional probability on random graphs using Markov chains. It defines key terms like Markov chains, probability matrices, and stationary probability vectors. The algorithm computes the steady state probability distribution for random graphs by creating a probability matrix and taking its powers to calculate successive stationary vectors until convergence. Several examples are provided to demonstrate calculating the steady state for unweighted and weighted random graphs, as well as bipartite graphs by changing the starting set of vertices.