Files and links (1)
SubmittedOpen Access (License Unspecified), Open
Journal article
Characterizing and Mining the Citation Graph of the Computer Science Literature
Knowledge and information systems, v 6(6), pp 664-678
01 Nov 2004
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.
Metrics
9 Record Views
Details
- Title
- Characterizing and Mining the Citation Graph of the Computer Science Literature
- Creators
- Yuan An - University of TorontoJeannette Janssen - Dalhousie UniversityEvangelos E. Milios - Dalhousie University
- Publication Details
- Knowledge and information systems, v 6(6), pp 664-678
- Publisher
- Springer Nature
- Number of pages
- 15
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000206578200002
- Other Identifier
- 991020547660704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems