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Composing Scientific Collaborations Based on Scholars’ Rank in Hypergraph

  • Fahimeh Ghasemian
  • Kamran Zamanifar
  • Nasser Ghasem-Aghaee
Article
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Abstract

Finding the right scholars for collaboration is crucial for scientific progress. In this study, a novel algorithm is proposed to find the successful team configurations for scientific collaboration in the presence of the collaboration network of scholars. In this algorithm, the collaboration network is exploited to estimate the trust level among team members and the skill level of the scholars, while a hypergraph is used to model the relations. Also, our algorithm improves the search process by directing it to the promising regions, where the probability of finding the successful teams is high. A comparison with other algorithms is done to evaluate the proposed algorithm, using the similarity to successful collaborations. Our findings show that this algorithm achieves a significantly higher performance, compared to the other algorithms.

Keywords

Scientific collaboration Collaboration network Hypergraph Jaccard similarity Team formation algorithm 

References

  1. Abbasi, A., Wigand, R.T., & Hossain, L. (2014). Measuring social capital through network analysis and its influence on individual performance. Library & Information Science Research, 36(1), 66–73.CrossRefGoogle Scholar
  2. Awal, G.K., & Bharadwaj, K. (2014). Team formation in social networks based on collective intelligence–an evolutionary approach. Applied Intelligence, 41(2), 627–648.CrossRefGoogle Scholar
  3. Bennett, L.M., & Gadlin, H. (2012). Collaboration and team science. Journal of Investigative Medicine, 60 (5), 768–775.CrossRefGoogle Scholar
  4. Bennett, L.M., Gadlin, H., & Levine-Finley, S. (2010). Collaboration Collaboration & team science: A field guide. NIH Office of the Ombudsman, Center for Cooperative Resolution.Google Scholar
  5. Börner, K., Contractor, N., Falk-Krzesinski, H.J., Fiore, S.M., Hall, K.L., Keyton, J., Spring, B., Stokols, D., Trochim, W., & Uzzi, B. (2010). A multi-level systems perspective for the science of team science. Science Translational Medicine, 2(49), 49cm24.CrossRefGoogle Scholar
  6. Börner, K., Conlon, M., Corson-Rikert, J., & Ding, Y. (2012). VIVO: A semantic approach to scholarly networking and discovery. Synthesis Lectures on the Semantic Web: Theory and Technology, 7(1), 1–178.CrossRefGoogle Scholar
  7. Bozeman, B., Fay, D., & Slade, C.P. (2013). Research collaboration in universities and academic entrepreneurship: the-state-of-the-art. The Journal of Technology Transfer, 38(1), 1–67.CrossRefGoogle Scholar
  8. Chen, J.V., Lu, I.H., Yen, D.C., & Widjaja, A.E. (2016). Factors affecting the performance of internal control task team in high-tech firms. Information Systems Frontiers, 116.Google Scholar
  9. de Vreede, G.J., Antunes, P., Vassileva, J., Gerosa, M.A., & Wu, K. (2016). Collaboration technology in teams and organizations: Introduction to the special issue. Information Systems Frontiers, 18(1), 1–6.Google Scholar
  10. Dorn, C., & Dustdar, S. (2010). Composing near-optimal expert teams: A trade-off between skills and connectivity. On the Move to Meaningful Internet Systems: OTM 2010, 472–489.Google Scholar
  11. Eslami, H., Ebadi, A., & Schiffauerova, A. (2013). Effect of collaboration network structure on knowledge creation and technological performance: the case of biotechnology in Canada. Scientometrics, 97(1), 99–119.CrossRefGoogle Scholar
  12. Falagas, M.E., Kouranos, V.D., Arencibia-Jorge, R., & Karageorgopoulos, D.E. (2008). Comparison of SCImago journal rank indicator with journal impact factor. The FASEB Journal, 22(8), 2623–2628.CrossRefGoogle Scholar
  13. Ghasemian, F., Zamanifar, K., Ghasem-Aghaee, N., & Contractor, N. (2016). Toward a better scientific collaboration success prediction model through the feature space expansion. Scientometrics, 1–25.Google Scholar
  14. Lappas, T., Liu, K., & Terzi, E. (2009). Finding a team of experts in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 467–476). ACM.Google Scholar
  15. Olson, G.M., Zimmerman, A., & Bos, N. (2008). Scientific collaboration on the Internet. The MIT Press.Google Scholar
  16. Schleyer, T., Butler, B.S., Song, M., & Spallek, H. (2012). Conceptualizing and advancing research networking systems. ACM Transactions on Computer-Human Interaction (TOCHI), 19(1), 2.CrossRefGoogle Scholar
  17. Sharma, A., Srivastava, J., & Chandra, A. (2014). Predicting multi-actor collaborations using hypergraphs. arXiv:1401.6404.
  18. Sharan, U., & Neville, J. (2008). Temporal-relational classifiers for prediction in evolving domains. In 2008 8th IEEE international conference on data mining (pp. 540549). IEEE.Google Scholar
  19. Skilton, P. (2008). Does the human capital of teams of natural science authors predict citation frequency? Scientometrics, 78(3), 525.CrossRefGoogle Scholar
  20. Stokols, D., Hall, K.L., Taylor, B.K., & Moser, R.P. (2008a). The science of team science: overview of the field and introduction to the supplement. American Journal of Preventive Medicine, 35(2), S77–S89.Google Scholar
  21. Stokols, D., Misra, S., Moser, R.P., Hall, K.L., & Taylor, B.K. (2008b). The ecology of team science: understanding contextual influences on transdisciplinary collaboration. American Journal of Preventive Medicine, 35 (2), S96–S115.Google Scholar
  22. Sun, Y., Tan, W., Li, L., Shen, W., Bi, Z., & Hu, X. (2016). A new method to identify collaborative partners in social service provider networks. Information Systems Frontiers, 18(3), 565–578.CrossRefGoogle Scholar
  23. Wang, X., Zhao, Z., & Ng, W. (2015). A comparative study of team formation in social networks. In Database systems for advanced applications (pp. 389404). Springer.Google Scholar
  24. Tan, S., Bu, J., Chen, C., & He, X. (2011). Using rich social media information for music recommendation via hypergraph model. In Social media modeling and computing (pp. 213237). Springer.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Fahimeh Ghasemian
    • 1
  • Kamran Zamanifar
    • 1
  • Nasser Ghasem-Aghaee
    • 1
  1. 1.Department of Software Engineering, Faculty of Computer EngineeringUniversity of IsfahanIsfahanIran

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