Abstract

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.

Original languageEnglish (US)
Title of host publicationKDD '09
Subtitle of host publicationProceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages817-825
Number of pages9
DOIs
StatePublished - Nov 9 2009
Event15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris, France
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
CountryFrance
CityParis
Period6/28/097/1/09

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Keywords

  • Behavior prediction
  • Modularity
  • Relational learning
  • Social dimensions
  • Social media

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Tang, L., & Liu, H. (2009). Relational learning via latent social dimensions. In KDD '09: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 817-825). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1557019.1557109