Toward Collective Behavior Prediction via Social Dimension Extraction

Collective behavior refers to how individuals behave when they are exposed in a social network environment. In this article, we examine how we can predict online behaviors of users in a network, given the behavior information of some actors in the network. Many social media tasks can be connected to the problem of collective behavior prediction. Since connections in a social network represent various kinds of relations, a social-learning framework based on social dimensions is introduced. This framework suggests extracting social dimensions that represent the latent affiliations associated with actors, and then applying supervised learning to determine which dimensions are informative for behavior prediction. It demonstrates many advantages, especially suitable for large-scale networks, paving the way for the study of collective behavior in many real-world applications.