Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches
arXiv:1107.5266 · doi:10.1371/journal.pone.0033255
Abstract
We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three pre-defined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.
18 pages, 9 figures
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- Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence
- Topics as Clusters of Citation Links to Highly Cited Sources: The Case of Research on International Relations
- Bibliometric Networks
- Evaluating Overlapping Communities with the Conductance of their Boundary Nodes
- Estimating Thematic Similarity of Scholarly Papers with Their Resistance Distance in an Electric Network Model