A CoEvolutionary Model for Inferring Online Social Network User Behaviors Accurate online social network user behavior inference can improve the performance of the other applications significantly, such as friend commendation, hot topic prediction, and personal website assistant. Previous works mainly focus on the trend analysis of user behaviors or adopt the method to fit the supposed userbehavior distribution, but they ignore the dynamic mutual influence among the users and behaviors on social networks. This paper proposes a coevolutionary model to formulate the interaction pattern among the users and behaviors, in which a systematic method is used for embedding the distinctiveness and permanence properties of the users and behaviors into latent features. This model could naturally capture the dynamic evolving process of the user behaviors with the time. Whatxe2x80x99s more, we also take into account the following relationship to depict the interaction information among users. Extensive experiments show that our algorithm achieves 0.024 of the MAE (hour) in the crime time inference, and 0.506 and 0.579 of the accuracy in the user and behavior inference, which surpass the stateofarts more than 7.19xc3x97, 1.12xc3x97 and 1.14xc3x97, respectively. Additional experiments on different training echoes of our model are provided to further explore its effectiveness and scalability.

Mining Crossplatform User Behaviors for Demographic Attribute Inference In this paper we seek to utilize the behavior information and the attribute labels of users from an auxiliary platform to help make attribute prediction. We propose a crossplatform demographic attribute inference model (CDAIM for short), in which we first learn the user representations with the behavior data, and then regulate the vectors of the same users from both platform to be close via a transfer function, and finally train a classifier with the feature vectors and attribute labels of all the users. We conduct extensive experiments on real datasets and the results show that our CDAIM outperforms the baselines.