Paper Short Abstract

Evaluating research performance is crucial for assessing academic contributions and impact. Traditional bibliometric methods often fail to capture complex patterns in citation networks and author collaborations. Generative Adversarial Networks (GANs) will enhance research metrics evaluation easily.

Paper Abstract

Assessing research performance is essential for understanding academic contributions, citation impact, and researcher productivity. Traditional bibliometric approaches rely on citation counts, h-index, and impact factors, but they often struggle to capture the complex, dynamic nature of scholarly influence. This study introduces a novel framework leveraging Generative Adversarial Networks (GANs) to enhance research metrics evaluation. The proposed GAN-based model synthesizes realistic citation patterns, detects anomalies in research impact, and predicts future trends with greater accuracy. By training on extensive bibliometric datasets, the model effectively learns underlying citation structures and academic network behaviors. Experimental validation demonstrates that the GAN-based approach improves ranking precision, detects citation manipulation, and provides a more holistic evaluation of research performance than conventional methods. Furthermore, this model facilitates early identification of influential works and emerging research domains, offering valuable insights for institutions, funding agencies, and policymakers. The proposed method represents a significant advancement in data-driven research assessment, moving beyond static metrics toward adaptive, AI-powered evaluation techniques. Integrating deep learning with bibliometric analysis provides a more robust and comprehensive framework for analyzing the scholarly impact, ensuring fairer and more accurate assessments of academic contributions. The GAN-based model generates synthetic citation patterns and predicts future research trends, enabling a more accurate and dynamic assessment of scholarly impact. Experimental results demonstrate improved precision in ranking researchers and publications compared to conventional methods. This framework offers a data-driven approach to refining research evaluation systems.

Keywords: Research Metrics, GAN, Citation Analysis, Scholarly Impact, Neural Networks, Academic Evaluation.