Abstract

The goal of this research is to implement network ranking using PageRank and random walk in Python. Network ranking is used to calculate the importance of nodes in the network. It helps to order the nodes according to their ranking values. Network ranking is performed using PageRank and random walk. The final results are compared to make sure that the two methods are matching. The basic steps of network ranking using PageRank and random walk are explained: defining network (nodes, adjacency matrix, and rank vector), computing outgoing nodes, computing transition matrix, performing PageRank, performing random walk, comparing results, and plotting charts. The developed program was tested on an experimental data. The program has successfully performed the basic steps of network ranking using PageRank and random walk and provided the required results.

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