Mohadeseh Rafiee

Academic Center for Education, Culture and Research

Papers

Total Citations

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About

Mohadeseh Rafiee is an emerging researcher whose work sits at the dynamic intersection of machine learning and bibliometric analysis. Her primary research focus is on graph neural networks (GNNs), a cutting-edge area of deep learning that models relational data. Her most cited work, "Graph Neural Networks: a bibliometrics overview" (2022), with 16 citations, makes a significant contribution by providing the first comprehensive, Scopus-based bibliometric mapping of the GNN research landscape. This study traces the evolution of the field from its inception in 2004, offering a quantitative and qualitative assessment of its growth, key contributors, and thematic trends. By systematically charting the rise of GNNs, Rafiee’s work serves as a vital roadmap for new researchers entering this fast-moving domain, helping them understand the field’s intellectual structure and emerging frontiers. Her research effectively bridges the gap between advanced computational methods and the science of research evaluation, demonstrating a keen ability to synthesize large-scale publication data into actionable insights. As GNNs continue to revolutionize fields from drug discovery to social network analysis, Rafiee’s bibliometric contributions provide a foundational perspective on where the field has been and where it is headed.