document: Relational learning via latent social dimensions Social media such as blogs , Facebook , Flickr , etc. , presents data in a network format rather than classical IID distribution . To address the interdependency among data instances , relational learning has been proposed , and collective inference based on network connectivity is adopted for prediction . However , connections in social media are often multi-dimensional . An actor can connect to another actor for different reasons , e.g. , alumni , colleagues , living in the same city , sharing similar interests , etc. . Collective inference normally does not differentiate these connections . In this work , we propose to extract latent social dimensions based on network information , and then utilize them as features for discriminative learning . These social dimensions describe diverse affiliations of actors hidden in the network , and the discriminative learning can automatically determine which affiliations are better aligned with the class labels . Such a scheme is preferred when multiple diverse relations are associated with the same network . We conduct extensive experiments on social media data ( one from a real-world blog site and the other from a popular content sharing site ) . Our model outperforms representative relational learning methods based on collective inference , especially when few labeled data are available . The sensitivity of this model and its connection to existing methods are also examined .
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