Goyal et al., 2010, Learning Influence Probabilities In Social Networks

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Online version

An online version of this paper is available at the [ACM digital library].

Summary

Influence propagation in social networks has interesting application, especially for viral marketing. Most past studies assume as input a graph with nodes for each person, and weighted edges between the nodes if there is influence between the two persons. However, less attention has been put on how to build this graph using social media data. This paper introduces a model of influence built using social graph data on one hand, and a log of action (e.g., joining a community) on the other hand. The model is validated on the Flick dataset, which consists in a social graph with 1.3M nodes/40M edges and action log of 300K distinct actions.

Key Contributions

The biggest contribution claimed by the authors in this paper is the evidence of influence in social networks.

Experiments and Evaluation

UsesMethod

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