2) Ms. Miriam Moscovici

This work presents a system for identifying key researchers in academic fields by applying Social Network Analysis (SNA) on citation data. Leveraging the comprehensive, open-access bibliographic database OpenAlex, the system constructs personalized, author-level citation networks using a method designed to capture the layered relationships among citations and citing authors. A composite ranking algorithm, which combines multiple centrality measures, including Degree, Weighted In-degree, Weighted Out-degree, and Eigenvector centrality, is then applied to rank researchers based on their influence, visibility, and structural role within the network. The system’s output is an interactive, web-based interface that provides a ranked list of influential researchers. The effectiveness of this approach was empirically validated through an evaluation study involving 71 academic researchers from leading Israeli universities. Each participant assessed researcher’s contributions using a Likert-scale, taking into account their importance and centrality within the academic discourse. Statistical analysis showed that approximately 50% of participant ratings fell into the highest contribution categories, with top- ranked researchers consistently receiving the highest evaluations. Logistic regression and generalized linear mixed-effects models confirmed an alignment between the system’s algorithmically generated rankings and participants’ subjective perceptions, with more prolific researchers showing greater agreement with the rankings. Lower-ranked researchers were more frequently marked as ‘Not Familiar,’ indicating that the rankings also reflected participants’ familiarity with the researchers. While the system effectively identifies influential figures at a broad field level, qualitative feedback suggests opportunities for refinement to better capture highly specialized or niche research communities. The findings serve as proof of concept, demonstrating that the proposed network construction method, when combined with multiple centrality measures, offers a reliable approach for identifying key researchers within academic ecosystems.