Learning Influence Probabilities In Social Networks

Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr

• Type: Conference
• Year: 2010
• Where: WSDM
• Authors: Amit Goyal 0002, Francesco Bonchi, Laks V. S. Lakshmanan
• Keywords: Data Mining, Influence Propagation, Real Social Network, Social Graph, WSDM 2010