LINK PREDICTION MODEL FOR PAGE RANKING OF BLOGS

Abstract

Social Network Analysis is mapping and measuring of relationships and flows of information between people, organizations, computers, or other information or knowledge processing entities. Social media systems such as blogs, LinkedIn, you tube are allows users to share content media, etc. Blog is a social network notepad service with consider on user interactions. In this paper study the link prediction and page ranking using MozRank algorithm using blog websites. It finds out how all the websites on the internet link to each other with the largest Link Intelligence database. As link data is also a component of search engine ranking, understanding the link profile of Search Engine positioning. Here the MozRank algorithm is using backlinks from blog websites and linking websites quality. Good websites with many backlinks which linking the corresponding WebPage give highly value of MozRank. MozRank can be improved a web page's by getting lots of links from semi-popular pages or a few links from very popular pages. The algorithm for page ranking must work differently and MozRank is more comprehensive and accurate than Goggle’s page rank. Another tool is Open Site Explorer that is ability to compare five URL's against each other. Open Site Explorer’s Compare Link Metrics option is how one measures page level metrics, the other domain. This result can help to generate a chart form for the comparative URLs. A comparison chart of the important metrics for these pages is shown which makes it very clear and easy to compare the data between the five URL's.