Quantification of Social Blog Network using B-Rank technique & Blog Recommandation

Abstract

 Now a days, Among all communication services that Internet offers, one of the fastest growing are weblogs (abbreviated blogs) (Blood, 2000; Winer, 1999). blogging has become a common way for people to pub­lish content on the Internet. Because blogs are easy to use, people can rap­idly share their daily diaries, discuss the latest news, and express their opin­ions on numerous topics. Given this convenient platform, the number of blogs is increasing at a dramatic rate. Because blog growth is massive, blog readers can find numerous “hot blogs” on blog hosting sites, yet have no idea which ones contain the most informa­tive content. Such as Browsing among so much information is no easy task, even more when blogs are often updated several times a day, and generic search engines do not update their indices so frequently. Another drawback of current search engines is that they perform only string searches; they not include any semantic information .Some blog service provid­ers (BSPs) list hot blogs based on their number of visitors, but this indicator is weak and insufficient for determining blog popularity, which is a crucial issue in the massive blogosphere.

                   This project extracts real-world blog data and analyzes the interconnections within blog communities. Such inter­connections reveal blogger behaviors and visibilities, as well as blog popu­larity — all of which might relate to blog quality. Social blog network model on this interconnection structure between blogs and, on this basis, developed a popularity ranking method, called BRank.

While conducting several experiments to analyze various  explicit and implicit interconnec­tion structures and discover variability in the impact of interactions in dif­ferent communities should be made.