Social Network Analysis and Mining

, Volume 2, Issue 2, pp 169–187

Trends in science networks: understanding structures and statistics of scientific networks


    • Carnegie Mellon University
  • Kathleen M. Carley
    • Carnegie Mellon University
  • L. Richard Carley
    • Carnegie Mellon University
Original Article

DOI: 10.1007/s13278-011-0044-6

Cite this article as:
Kas, M., Carley, K.M. & Carley, L.R. Soc. Netw. Anal. Min. (2012) 2: 169. doi:10.1007/s13278-011-0044-6


The growing of availability of electronic resources over the Internet enables rapid dissemination of the ideas and changes in the trends and the interaction patterns. In this work, we focus on dynamic, evolving social networks which exhibit numerous features that are also of interest to many researchers in non-social fields such as statistical physics, biology, applied mathematics, and computer science. We investigate how a specific research area (high-energy physics) changes over time, by building complex, interlinked citation, publication, and co-publication networks that evolve and expand constantly through the emergence of new papers and authors. Following an interdisciplinary approach, we perform a wide-ranging analysis of the high-energy physics dataset using techniques such as social networks centrality analysis, topological analysis, investigation of power law characteristics, time series analysis of publication and collaboration frequencies, as well as spatiotemporal analysis to discuss relationships among involved countries.

Copyright information

© Springer-Verlag 2012