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Embedding multiple virtual networks (VNs) on the shared bottom is a challenge for cloud computing platforms and large-scale sliceable network testing platforms. In this paper, Markov random walk model is used to rank nodes according to their resources and topological attributes. This new topology-aware node ranking method can reflect the relative importance of nodes. Two VN embedding algorithms, RW-MaxMatch and RW-BFS, are designed by using node sorting. The simulation results show that compared with the existing embedding algorithms, the ranking of topology-aware nodes has better resource metrics, and the proposed RW-based algorithm increases the long-term average revenue and acceptance rate.