Evolution of User Activity and Community Formation in an Online Social Network

The paper performs an empirical study of the MySpace Online Social Network (OSN). It aims to capture the evolution of user population, to examine user activity, and finally to characterize community formation using two well established community finding algorithms, namely the Fortunato et al. and the Clique Percolation algorithms. Both algorithms are known to be effective in identifying communities in large graphs, starting at seed nodes and utilizing only local interactions between nodes. One million user profiles were randomly collected in a month's period. For each profile certain attributes were fetched: profile status (public, private, invalid), member since and last login dates, number of friends, number of views, etc. The profiles and their attributes were analyzed in order to reveal the evolution in user population and the activity of the participating members. Significant conclusions were drawn for the synthesis of the population based on profile status, the number of friends, and the duration MySpace members stay active. Subsequently, a large number of communities were identified aiming to reveal the structure of the underlying social network graph. The collected data were further analyzed in order to characterize community size and density but also to retrieve correlations in the activity among members of the same community. A total of 171 communities were detected with Fortunato's algorithm, while using Clique Percolation this number was 201. Results demonstrate that MySpace members tend to form dense communities. For the first time, strong correlation in the last login date (the main attribute that shows user activity) for members of the same community was documented. It was also shown that members participating in the same community have similar values for other attributes like for example number of friends. Lastly, there is strong evidence that participation of users in communities inhibits them from abandoning MySpace.