Evaluating Machine Learning Algorithms in COVID-19 Research: A Framework Based on Algorithm Co-Occurrence and Symmetric Network Analysis

Siqi Huang, Luoming Liang, Ying Zhao

Machine learning (ML) algorithms are reshaping academic research. However, there is a lack of systematic impact analysis in specific domains. We propose a framework for evaluating the knowledge landscape of domain-specific ML research. It consists of three key components: LDA (Latent Dirichlet Allocation) for topic identification, co-occurrence network construction, and influential algorithm scoring using centrality metrics. In a case study on COVID-19 research, we analyze 30,664 ML-related papers. We identify 13 research topics. We construct a symmetric undirected network to quantify algorithm influence. This analysis employs six centrality metrics: mention frequency, weighted degree, degree centrality, eigenvector centrality, closeness centrality, and betweenness centrality. Results were obtained following linear normalisation. The framework highlights the top ten most influential algorithms for each topic. It reveals the evolving impact of algorithms in COVID-19 research. The methodology is adaptable to other domains. It provides a systematic approach to understanding ML domain-specific impact.