Tracking the Evolution: Discovering and Visualizing the Evolution of Literature

A common task in research preparation is to survey related work in bibliographic databases. Scientists are finding the survey task notably difficult as the volume of the databases has been increased considerably over the past few decades. Making a good use of a survey paper of the research topic can vastly lower the difficulty but there may be no survey paper in some emerging research topics due to the rapid development. In this work, we propose a novel Literature Evolution Discovery (LED) process that aims to provide an explainable evolution structure of literature. The explainability is based on co-citation analysis and latent relationship extraction, which are done by Steiner tree algorithm and context-consistent factor graph model, respectively. The experiments show the superiority of our context-consistent factor graph model, compared with the state-of-the-art baselines. Our case studies and visualization results demonstrate the effectiveness and interpretability of our proposed algorithms in practice.