Learning Propagation Models from Social Networks

The emergence of Social Networks and Social Media sites has motivated a large amount of recent research. Different generic tasks are currently studied such as Social Network Analysis, Social Network annotation, Community Detection, Link Prediction. One classical question concerns the study of temporal propagation of information through this new type of media. It aims at studying how information propagates on a network. Many recent works are directly inspired from the literature in the epidemiologic domain or in the social science domain. These works mainly propose different propagation models – independent cascade models or linear threshold models – and analyze different properties of these models, such as the epidemic threshold. Recently, instead of analyzing how information spreads, different articles address the problem of predicting the propagation in the future ([4, 5]). This is a key problem with many applications like Buzz prediction – predicting if a particular content is a buzz – or Opinion Leader Detection – detecting if a node in a network will well spread content.  This challenge analyzed and compared the quality of propagation prediction methods. The challenge was organized in order to facilitate the participation of any interested researcher, by providing simple tools and easy to use datasets. We anticipate that the produced material can become the first large benchmark for propagation models.