A Distributed Tool for Online Identification of Communities in Co-authorship Networks at a University

  • David Fernandes
  • Nuno DavidEmail author
  • Maria João Cortinhal
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 319)


Most universities have their public repositories of scientific publications available online. The data is made available raw or by department listing and does not provide the network of co-authorships that implicitly emerges from scientific collaborations among different departments. Sometimes, the network of co-authorships is computed within the institution, via standalone applications that have few or no functionalities to explore the structure of collaborations. The possibility of searching online and managing the network of scientific communities in the institution is a matter of management efficiency, both for the institution itself and other external collaborators. This paper explains a distributed architecture and a tool that uses data from an online institutional repository. The tool calculates and puts available online the co-authorship network that identifies research communities according to different algorithms. The tool reflects and identifies the emergent structure of communities, graphically analyses communities, exports, reports and follows up with the evolution of communities in time.


Online institutional repositories Community detection tools Interdisciplinary collaboration Co-authorship Graph Author Publication ABCD and MCL algorithms 


  1. Ciência-IUL: Documentação da API Pública November 2018.
  2. Dongen, S.v.: Graph Clustering by Flow Simulation. University of Utrecht (2000)Google Scholar
  3. Dongen, S.v.: MCL - a cluster algorithm for graphs January 2017.
  4. GEXF Working Group: GEXF File Format (2018).
  5. Jackson, M.O.: Social and Economic Networks. Princeton University Press (2008)Google Scholar
  6. Liu, X., Bollen, J., Nelson, M.L., Sompel, H.V.: Co-authorship networks in the digital library research. Community Inf. Process. Manage. 41(6), 1462–1480 (2005)Google Scholar
  7. MongoDB: MongoDB (2017, 2 6).
  8. Newman, M.E.: Co-authorship networks and patterns of scientific collaboration. In: Proceedings of the National Academy of Sciences, pp. 5200–5205 (2004)Google Scholar
  9. Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and central. Phys. Rev. E 64(1), 01632 (2001)Google Scholar
  10. Node.js: Node.js (2017, 2 6).
  11. Ruifang Liua, S.F.: Weighted graph clustering for community detection of large social networks. In: 2nd International Conference on Information Technology and Quantitative Management, ITQM (2014)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  • David Fernandes
    • 1
  • Nuno David
    • 1
    • 2
    Email author
  • Maria João Cortinhal
    • 1
    • 3
  1. 1.University Institute of Lisbon – ISCTE-IULLisbonPortugal
  2. 2.Dinamia-CET ISCTE-IULLisbonPortugal
  3. 3.CMAF-CIO, Faculdade de Ciências da Universidade de LisboaLisbonPortugal

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