Complex networks play an important role in anyone’s life. Most of real systems such as social, communicational and biological systems, can be modeled using these complex networks. Random walk is one of the important statistical methods for this purpose. In the recent years, random walks have been a convenient approach for solving a variety of problems in a vast discipline such as electronics, traffic control, biology, chemistry, physics, psychology, economy etc. By now different kind of random walks have been using for studying complex networks. For all these random walks, the way of distributing a certain number of them on complex networks is vital. Analytical and computational pursuing of a normal random walk and a random walk with stopping probability has led to a discovery which is; the random walks distribution on different networks varies linearly depend on the degree of network vertices. Computational results state that for a particular network with a constant number of vertices, slope of the number of random walks verses degree of vertices decreases as the number of edges increase.