Analysis On Community Structure Of Online Social Networks

The Internet has spawned many different types of information sharing systems, including the Web. Recently, online social networks have gained significant popularity and are now among the most popular sites on the Web. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks.In this paper we have studied a popular online social network: YouTube. The data was gained by BFS (Breadth-first search) . The focuses of this paper is detecting the community of YouTube with label propagation algorithm and improve the algorithm. We also analyze the characteristics of communities. Our experiments prove that label propagate algorithm is an effective method to detect communities in large-scale networks. And the partitions are superior. As the stop requisition being stricter, the circulation time is increased rapidly. In order to solve this problem, we interrupt and change the direction of the process. The result is satisfied as long as we chose the suitable time to intervene. Besides that we compare different partitions with f coefficient and Jaccard Index. We concluded that although there are more than one partition in the same begin and stop conditions. The partitions are very similar. Then we analyze the relation between community and group. The conclusion is that users which belong to the same group are inclined to belong to the same community. The users which are divided to the same community are always members of the same group vice-versa. In the end of the paper, we point out the directions of next step.