Knowledge and Information Systems

, Volume 6, Issue 6, pp 664-678

First online:

Characterizing and Mining the Citation Graph of the Computer Science Literature

  • Yuan AnAffiliated withDepartment of Computer Science, University of Toronto
  • , Jeannette JanssenAffiliated withDepartment of Mathematics and Statistics, Dalhousie University
  • , Evangelos E. MiliosAffiliated withFaculty of Computer Science, Dalhousie University Email author 

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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.


Citation graph Graph connectivity Networked information spaces Power law Small worlds