Finding Healthcare Provider Experts via Sheaf Laplacian
Mehmet Emin Aktas, Iraj Moradi, Esra Akbaş, Mehmet Boyno · 2025
Identifying experts within healthcare provider net-works is crucial for improving patient outcomes, optimizing resource allocation, and fostering medical collaboration. Traditional network-based expert detection methods primarily rely on centrality measures, which consider only structural connectivity without accounting for domain-specific expertise. In this paper, we propose a sheaf Laplacian-based expert detection method that ranks healthcare providers based on their expertise across multiple subdomains. After modeling healthcare provider networks as graphs and hypergraphs, using sheaf theory, we incorporate medical specialties into the network structure. The sheaf Laplacian diffusion model enables us to capture information propagation across providers by considering these medical specialties that facilitate a more refined ranking of experts. We evaluate our method on a benchmark dataset from the Stack Exchange healthcare community, comparing it with existing graph-based methods such as PageRank, the Susceptible-Infected-Recovered (SIR) model, and conventional Laplacian models. Experimental results using correlation and Hits@n metrics demonstrate that our sheaf-based graph and hypergraph models outperform these baselines.