Feature Selection for Community Evolution Prediction in Location-Based Social Network: Gowalla and Brightkite

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Over the past years, there is a growing concern in analyzing social networks and modeling their dynamics at different scales. Most social networks are dynamic and evolve gradually. Also, the communities in these dynamic networks usually have changing members and could grow and shrink over time. Therefore, one of the central challenges is to predict the future orientation of community evolution using the community features mined at different time intervals. Though, both the massive size of data and the dynamic nature of the network make it difficult to efficiently calculate these features. In this paper, we suggest a new approach that studies the structural and temporal features of the network and identify the most important subset of community features in order to predict the future orientation of communities in dynamic social networks. Our framework is to select the significant features associated to a community – its structure and history that guides to precise community event evolution. Contrasting to common methods that result huge number of features at each time interval, our suggested approach demands identifying essential number of community features to adequately define if a community will continue stable or experience certain events like shrinking, splitting or merging. Our experiments on real world datasets confirms the efficiency of the suggested framework.