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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.
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