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Computational Social Network Analysis pp 387–409Cite as

Analyzing Collaborations Through Content-Based Social Networks

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Part of the book series: Computer Communications and Networks ((CCN))

Abstract

This chapter presents a methodology and a software application to support the analysis of collaborations and collaboration content in scientific communities. High-quality terminology extraction, semantic graphs, and clustering techniques are used to identify the relevant research topics. Traditional and novel social analysis tools are then used to study the emergence of interests around certain topics, the evolution of collaborations around these themes, and to identify potential for better cooperation.

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Notes

  1. 1.

    Note that specialized domains are those with higher potential of application for social analysts: thematic blogs, research communities, networked companies, etc.

  2. 2.

    http://www.sioc-project.org

  3. 3.

    http://www.interop-vlab.eu

  4. 4.

    http://lcl.uniroma1.it./termextractor

  5. 5.

    http://lcl.uniroma1.it/glossextractor

  6. 6.

    The measure is described in detail in [4].

  7. 7.

    Singular value decomposition (SVD) is used to reduce data sparseness of the similarity matrix X.

  8. 8.

    Evaluation on data sets in different applications makes no sense, since k-means++ and Repeated Bisections have been already evaluated in the literature. What matters here is to measure the added value of the feature extraction methodology.

  9. 9.

    All the authors had direct responsibilities in the research network monitoring and management tasks.

  10. 10.

    The clustering tendency of concepts is measurable by computing the entropy of the related similarity vectors. Sparse distribution of values over the vector’s dimensions indicates low clustering tendency.

  11. 11.

    For example, if the analysis reveals that partner X and partner Y have common research interests but do not cooperate, this can easily be verified by looking at the partners’ publications and activities in the INTEROP knowledge-base, the KMap [18].

  12. 12.

    Clearly, if a document (a paper) is authored by members of different groups, it contributes to more than one centroid calculation.

  13. 13.

    http://jung.sourceforge.net

  14. 14.

    As a practical example, the partners from Ancona (UNIVPM-DIIGA) and Roma (UoR) were more oriented on research on natural language processing and on information retrieval, initially not a shared theme in the INTEROP community. During the project, a fruitful application of our techniques to interoperability problems has led to a better integration of our organizations within the NoE, as well as to the emergence of NLP-related concepts among the “hot” INTEROP research themes.

  15. 15.

    http://interop-vlab.eu/ei_public_deliverables/interop-noe-deliverables/

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Author information

Authors and Affiliations

  1. Polytechnic University of Marche, Ancona, Italy

    Alessandro Cucchiarelli & Fulvio D’Antonio

  2. “Sapienza Università di Roma”, Roma, Italy

    Paola Velardi

Authors
  1. Alessandro Cucchiarelli
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  2. Fulvio D’Antonio
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  3. Paola Velardi
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Corresponding author

Correspondence to Alessandro Cucchiarelli .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation &, Machine Intelligence Research Labs (MIR), Auburn, 98071-2259, U.S.A.

    Ajith Abraham

  2. Fac. Computers & Information, Cairo University, Ahmed Zewal Street 5, Giza, 12613, Egypt

    Aboul-Ella Hassanien

  3. Dept. Computer Science, Technical University Ostrava, Tr. 17. Listopadu 15, Ostrava, 708 33, Czech Republic

    Vaclav Sná¿el

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Cucchiarelli, A., D’Antonio, F., Velardi, P. (2010). Analyzing Collaborations Through Content-Based Social Networks. In: Abraham, A., Hassanien, AE., Sná¿el, V. (eds) Computational Social Network Analysis. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-84882-229-0_15

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  • DOI: https://doi.org/10.1007/978-1-84882-229-0_15

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-228-3

  • Online ISBN: 978-1-84882-229-0

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