Predicting the Evolution of Communities in Social Networks

Abstract

We studied the predictability of community evolution in on-line social networks as a supervised learning task with sequential and non-sequential classifiers. Communities that are formed in on-line social networks as a result of user interaction evolve over time. Structural, content and contextual features as well as the previous states of a community are considered as the features that are involved in the task of community evolution. The evolution phenomena we try to predict are the continuation, shrinking, growth and dissolution. The evolution labels stem from a community tracker that provided the background truth. We have obtained interesting results on a set from Twitter.