A comparative study on web page ranking algorithms.

The Internet is the source of information that first comes to mind for a large number of people today. Over the years the size of the internet has significantly increased. While the size of the surface web is estimated to be over fifty billion web pages, the deep web, on the other hand, is estimated to be more than thrice it's size. While it's impossible to list every URL that has ever existed or go through it for a single user, it is also highly inefficient to apply brute force while going through it. Search engine ranking algorithms are the tools that help the user reach the relevant content based upon their respective queries. While search engines can randomly pick the web URLs by just comparing the titles orsome random words, it's not recommended as not only is it unfair, it can also lead to performance issues, getting irrelevant data, but also a huge security threat and waste of the user's precious time. What's relevant today might just become irrelevant tomorrow. Not only that but also we can't use human employees to verify every web page either. Various search engine ranking algorithms come into play here and help the users to get the relevant information based on various factors like hyperlinks, popularity, distance between pages, relevance, compatibility, time etc. In the given paper we compare several such algorithms to find out their limitations and advantages that can help us in further research of relevant and more efficient web page ranking algorithms. [ABSTRACT FROM AUTHOR]