Fuzzy Social Network Analysis in Industry Academic Collaborations

Authors

  • N. Bhuvaneswari * Department of Mathematics, G.T.N. Arts College, Dindigul, Tamil Nadu.
  • P. Pandiammal Department of Mathematics, G.T.N. Arts College, Dindigul, Tamil Nadu.

https://doi.org/10.22105/scfa.v2i2.56

Abstract

Industry-academic collaborations form intricate networks of researchers, institutions, and knowledge exchange. Traditional Social Network Analysis (SNA) techniques often fail to capture the uncertainties and imprecise relationships in these networks. This research introduces a fuzzy graph-based approach to model industry-academic collaborations, where relationships are characterized by varying degrees of membership, trust, influence, and contribution. We explore applications such as co-authorship networks, research impact analysis, and interdisciplinary collaboration mapping. A case study on global academic networks is provided, demonstrating the effectiveness of fuzzy SNA in analyzing uncertain and evolving relationships.

Keywords:

Fuzzy graphs, Social network analysis, Industry-academic collaborations, Co-authorship networks, Research impact, Uncertainty modeling

References

  1. [1] Porreca, A., Maturo, F., & Ventre, V. (2025). Fuzzy centrality measures in social network analysis: Theory and application in a university department collaboration network. International journal of approximate reasoning, 176, 109319. https://doi.org/10.1016/j.ijar.2024.109319
  2. [2] Porreca, A., Maturo, F., & Ventre, V. (2024). Fuzzy social network analysis: Theory and application in a university department’s collaboration network. https://doi.org/10.48550/arXiv.2407.02401
  3. [3] Porreca, A., Maturo, F., & Ventre, V. (2024). Novel fuzzy centrality measures in vague social networks. International journal approx. reasoning, 176, 109319. https://doi.org/10.48550/arXiv.2407.02401
  4. [4] Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2024). Analyzing Social Networks. SAGE Publications Ltd. https://www.torrossa.com/en/resources/an/5730558
  5. [5] Nepusz, T., Petróczi, A., Négyessy, L., & Bazsó, F. (2008). Fuzzy communities and the concept of bridgeness in complex networks. Physical review e—statistical, nonlinear, and soft matter physics, 77(1), 16107. https://doi.org/10.1103/PhysRevE.77.016107
  6. [6] West, D. B. (2001). Introduction to graph theory (Vol. 2). Prentice Hall.
  7. [7] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  8. [8] Bondy, J. A., & Murty, U. S. R. (2008). Graph theory. Berlin: Springer. https://B2n.ir/wh1209
  9. [9] Diestel, R. (2005). Graph Theory. Springer. https://books.google.com/books/about/Graph_Theory.html?id=aR2TMYQr2CMC
  10. [10] Wasserman, S., & Faust, K. (2012). Social network analysis: Methods and applications. Cambridge University Press. https://B2n.ir/jy7219
  11. [11] Brandes, U. (2005). Network analysis: Methodological foundations. Springer Science & Business Media. https://B2n.ir/mk1395

Downloads

Published

2025-05-13

Issue

Vol. 2 No. 2 (2025): Soft Computing Fusion with Applications

Section

Articles

How to Cite

Fuzzy Social Network Analysis in Industry Academic Collaborations. (2025). Soft Computing Fusion With Applications , 2(2), 95-105. https://doi.org/10.22105/scfa.v2i2.56

Similar Articles

11-20 of 29

You may also start an advanced similarity search for this article.