Modeling the dynamics of scientific communities

What makes a scientific community grow or shrink, or an individual paper be cited more or less? We seek to automatically identify emergent technical areas based on the full text of scientific papers, including their citations. Achieving this goal will require significant advances in our understanding both of the dynamics of emergence and of the indicators by which it can be recognized. We characterize papers and communities by the language they use and their citation link strucdture. We are also modelingn the dynamics of the birth, growth, and death of scientific micro-communities based on novel algorithms for clustering millions of scientific papers based on their citations.

For more information

• Characterizing Emergence Using a Detailed Micro-model of Science: Investigating Two Hot Topics in Nanotechnology.
  Kevin W. Boyack, Richard Klavans, Henry Small and Lyle Ungar
  Technology Management for Emerging Technologies (PICMET) 2012.
• Positioning Knowledge: Schools of Thought and New Knowledge Creation.
  S. Phineas Upham, Lori Rosenkopf and Lyle H. Ungar,
  Scientometrics, 83(2) 555-581, 2010.
• Innovating knowledge communities - An analysis of group collaboration and competition in science and technology.
  Phineas Upham, Lori Rosenkopf, Lyle H. Ungar
  Scientometrics 83(2): 525-554, 2010.
• Analyzing knowledge communities using foreground and background clusters.
  V. Kandylas, S. Upham, and L.H. Ungar,
  ACM Transactions on Knowledge Discovery from Data (TKDD) 4(2), 1-35, 2010.
• Finding cohesive clusters for analyzing knowledge communities.
  Vasileios Kandylas, S. Phineas Upham, and Lyle H. Ungar,
  Seventh IEEE International Conference on Data Mining (ICDM), Oct 2007.
• Innovating Knowledge Communities.
  Phin Upham, Lori Rosenkopf, and Lyle Ungar,
  2007 Academy of Management Meeting, Philadelphia, PA, 2007
  (selected for the ``Best Paper Proceedings of the 2007 Academy of Management Meeting.'')