Page Rank Algorithm using Directed Graph

PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.

• PageRank is a function that assigns a real number to each page in the Web (or at least to that portion of the Web that has been crawled and its links discovered). 
• The intent is that the higher the PageRank of a page, the more “important” it is. 
• There is one most popular model known as Random Surfer Model 

Consider a directed graph with 4 nodes A,B&C and D ,in which D is a Dangling node

For Example: Consider a directed graph with 3 nodes A,B & C 

Iteration 1:

Iteration 2:

The output of all the iteration in one table as shown: 

Trick to find the Page Rank :

For the above given graph with ABC nodes 

Page rank of a node is given by 

indegree(name of the node)/(name of the node outdegree)

Page rank of A = C / 1

Page rank of B = A / 2

Page rank of C = A/2 + B/1

Iteration      A      B      C

   0              1      1      1

   1              1      0.5   1.5

   2              1.5   0.5   1