Some time ago I wrote about the Google PageRank algorithm in Python. It's a matrix algorithm for calculating the PageRank values for every page in a web. All you have to do is define which pages links to which and the algorithm calculates the PageRanks for every page for you.
Now I'm going to try to illustrate it in practise for those of you who don't know what to do with a "Python script"n:/plog/blogitem-040321-1/PageRank.py.
See the gallery of previous calculations.
The purpose of this simple script is to convert the web matrix that you entered into a directed graph showing the approximated PageRank value for every node.
What you can do with this is to test how the PageRank algorithm works graphically. You might want to know what the effect is to be linked to by one very popular page or the effect of being linked by several not so popular pages. It's up to you to draw your own conclusions.
The input is limited in size (to save my poor computer) and the graphs aren't beautiful. (Thanks Ero Carrera for pydot which made this possible)
One conclusion I've drawn is that PageRank is very contagious.
For example. www.slashdot.org has a very high PageRank, but to get them to link to your site on the front page is hard. Having your link on one of the articles linked from the frontpage means increased PageRank for you even though that article itself is not linked to from many other pages.
can we jointly build a tool to calculate possible pagerank of a page before google updates its page rank based on number of backlinks it has? contact me at http://topserve.com.ng/contact