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Local bilateral clustering for identifying research topics and groups from bibliographical data


The structure of scientific collaboration networks provides insight on the relationships between people and disciplines. In this paper, we study a bipartite graph connecting authors to publications and extract from it clusters of authors and articles, interpreting the author clusters as research groups and the article clusters as research topics. Visualisations are proposed to ease the interpretation of such clusters in terms of discovering leaders, the activity level, and other semantic aspects. We discuss the process of obtaining and preprocessing the information from scientific publications, the formulation and implementation of the clustering algorithm, and the creation of the visualisations. Experiments on a test data set are presented, using an initial prototype implementation of the proposed modules.

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    As the graph is bipartite, necessarily \(\varGamma (v) \subseteq T\) as well as \(\varGamma (w) \subseteq T\).

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    In the symmetric mode, once a cluster is computed, the included vertices are no longer available for inclusion in future cluster computations.

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    The weight of an edge w(vu) is computed as the multiplicity of that edge; for purposes of the clustering phase, the edges are treated as directed and the weight is normalised by the degree of vertex v, making the directed edge weight asymmetric.

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    For example, the Wordle tool (

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    Available at in XML format.

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    In a systematic sample, each element is chosen after k steps, where k results from dividing the total number of elements by the desired sample size.

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    Only iterations where the cluster order was above the threshold were considered.


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The first author was supported by SEP-PROMEP Grant No. 103.5/12/7884. We thank the anonymous reviewers for their useful suggestions that helped improve the manuscript.

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Correspondence to Satu Elisa Schaeffer.

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Villarreal, S.E.G., Schaeffer, S.E. Local bilateral clustering for identifying research topics and groups from bibliographical data. Knowl Inf Syst 48, 179–199 (2016).

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  • Clustering
  • Knowledge discovery
  • Collaboration networks
  • Network analysis