PageRank algorithm is one of the most commonly used algorithms that determine the global importance of web pages. Due to the size of web graph which contains billions of nodes, computing a PageRank vector is very computational intensive and it may takes any time between months to hours depending on the efficiency of the algorithm. This promoted many researchers to propose techniques to enhance the PageRank algorithm. The researchers investigated all aspects of PageRank algorithm that covers stability, convergence speed, memory consumption, I/O efficiency, and the connectivity matrix properties [2-7, 11]. However, some aspects of PageRank algorithm are left unstudied. In addition, very few techniques are building on the results of the others. In this work we investigate some PageRank properties and report our findings.
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