2009 RelationalLearningviaLatentSoci

Subject Headings:

Notes

Categories and Subject Descriptors: H.2.8 Database Management: Database applications — Data Mining; J.4 [[Social and Behavioral Sciences: Sociology.
General Terms: Algorithm, Experimentation

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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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 RelationalLearningviaLatentSoci	Huan Liu Lei Tang			Relational Learning via Latent Social Dimensions		KDD-2009 Proceedings		10.1145/1557019.1557109		2009