When a novel research topic emerges, we are interested in discovering how the topic will propagate over the bibliography network, i.e., which author will research and publish about this topic. Inferring the underlying influence network among authors is the basis of predicting such topic adoption. Existing works infer the influence network based on past adoption cascades, which is limited by the amount and relevance of cascades collected. This work hypothesizes that the influence network structure and probabilities are the results of many factors including the social relationships and topic popularity. These heterogeneous information shall be optimized to learn the parameters that define the homogeneous influence network that can be used to predict future cascade. Experiments using DBLP data demonstrate that the proposed method outperforms the algorithm based on traditional cascade network inference in predicting novel topic adoption.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Biru Cui
    • 1
  • Shanchieh Jay Yang
    • 1
  1. 1.Department of Computer EngineeringRochester Institute of TechnologyRochesterUnited States

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