Remodeling the network for microgroup detection on microblog

In this paper, we focus on the problem of community detection on Sina weibo, the most popular microblogging system in China. By characterizing the structure and content of microgroup (community) on Sina weibo in detail, we observe that different from ordinary social networks, the degree assortativity coefficients are negative on most microgroups. In addition, we find that users from the same microgroup tend to share some common attributes (e. g., followers, tags) and interests extracted from their published posts. Inspired by these new findings, we propose a united method to remodel the network for microgroup detection while maintaining the information of link structure and user content. Firstly, the link direction is concerned by assigning greater weight values to more surprising links, while the content similarity is measured by the Jaccard coefficient of common features and interest similarity based on Latent Dirichlet Allocation model. Then, both link direction and content similarity between two users are uniformly converted to the edge weight of a new remodeled network, which is undirected and weighted. Finally, multiple frequently used community detection algorithms that support weighted networks could be employed. Extensive experiments on real-world social networks show that both link structure and user content play almost equally important roles in microgroup detection on Sina weibo. Our method outperforms the traditional methods with average accuracy improvement up to 39%, and the number of unrecognized users decreased by about 75%.