Now I've done something relatively useful with my PageRank algorithm written in Python that I'm actually quite proud of. It's not rocket science but at least I've managed to understand the Google PageRank algorithm and applied it to my own setup. This application is very simple and not so useful as one could hope but at least I prove to myself that it can be done.
I call it PlogRank. As you might have noticed, most blog items here on this site have on the left hand side, beneath the menu, a list of "Related blogs". These are from now on sorted by PlogRank! Cool, ha?
The "Related blogs" work by specific word matching. Every blog item has a list keywords that I define manually through the management interface. The selection of keywords is helped by another little database that filters out all typical words. E.g. "PageRank" is a particular word and "page" is not; so selecting these keywords is very easy for me.
Anyway. What I do now, once every week, is that I load a huge matrix of all connections between pages. If this blog item has a link to PageRank in Python then that page increases in PlogRank. It does not effect this page. I then feed this into the PageRanker program I've written which calculates the corresponding PageRank for each blog item. Easy! The whole calculation takes only a couple of seconds with 30 iterations. The calculation is actually only a small part of that time because reading from and writing to the database is the real bottleneck.
So, the end result is that every blog item that has related links will show these links in PlogRank-sorted order. Isn't that neat?Follow @peterbe on Twitter