Knowledge and Information Systems

, Volume 6, Issue 6, pp 664–678

Characterizing and Mining the Citation Graph of the Computer Science Literature

Authors

  • Yuan An
    • Department of Computer ScienceUniversity of Toronto
  • Jeannette Janssen
    • Department of Mathematics and StatisticsDalhousie University
    • Faculty of Computer ScienceDalhousie University
Article

DOI: 10.1007/s10115-003-0128-3

Cite this article as:
An, Y., Janssen, J. & Milios, E. Know. Inf. Sys. (2004) 6: 664. doi:10.1007/s10115-003-0128-3

Abstract

Citation graphs representing a body of scientific literature convey measures of scholarly activity and productivity. In this work we present a study of the structure of the citation graph of the computer science literature. Using a web robot we built several topic-specific citation graphs and their union graph from the digital library ResearchIndex. After verifying that the degree distributions follow a power law, we applied a series of graph theoretical algorithms to elicit an aggregate picture of the citation graph in terms of its connectivity. We discovered the existence of a single large weakly-connected and a single large biconnected component, and confirmed the expected lack of a large strongly-connected component. The large components remained even after removing the strongest authority nodes or the strongest hub nodes, indicating that such tight connectivity is widespread and does not depend on a small subset of important nodes. Finally, minimum cuts between authority papers of different areas did not result in a balanced partitioning of the graph into areas, pointing to the need for more sophisticated algorithms for clustering the graph.

Keywords

Citation graph Graph connectivity Networked information spaces Power law Small worlds

Copyright information

© Springer-Verlag 2004