Importance ranking of social network nodes based on PageRank
Abstract
Social network service (SNS) is an information sharing platform based on real-life friend
relationship. With the rapid development of Internet industry, and benefit from the
advantages of social network information dissemination, more and more people choose to
use social network, making it one of the important fields of Internet industry. As the
establishment of social network is based on the real friend relationship, it has both the
attributes of network structure and the characteristics of social network.
At present, famous social network platforms at home and abroad include twitter, youtube,
Facebook, Sina Weibo, etc. As the most famous social network in China, Sina Weibo has a
monthly active user of 516 million by the end of 2019. There are a large number of
information published on the platform every day. Weibo plays an important role in
information dissemination. At present, it has obviously surpassed the traditional media, so
Sina Weibo network has an important research value.
The main work of this paper includes: Firstly, the small world effect, scale-free
characteristics, six degree segmentation theory , Rule of 150 and the introduction of the
analysis methods of social network; secondly, collects the information of microblog users
with the method of web crawler , and preliminarily sort the data and set it into the database;
then, introduces the Markov process, expounds the basic idea of PageRank algorithm, and
explains the use of Markov process in calculating the PR value; finally, applies the
PageRank algorithm to the data before and obtain user's PR value so user importance is
sorted.
Keywords: social network, Sina Weibo, user importance, PageRank algorithm