Inferring the Underlying Structure of Information Cascades

In social networks, information and influence diffuse among users as cascades. While the importance of studying cascades has been recognized in various applications, it is difficult to observe the complete structure of cascades in practice. Moreover, much less is known on how to infer cascades based on partial observations. In this paper, the authors study the cascade inference problem following the independent cascade model, and provide a full treatment from complexity to algorithm they propose the idea of consistent trees as the inferred structures for cascades; these trees connect source nodes and observed nodes with paths satisfying the constraints from the observed temporal information.

Provided by: Cornell University Topic: Big Data Date Added: Oct 2012 Format: PDF

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