Co-authorship network analysis in cardiovascular research utilizing machine learning (2009-2019)

Background: With the recent advances in computational science, machine-learning methods have been increasingly used in medical research. Because such projects usually require both a clinician and a computational data scientist, there is a need for interdisciplinary research collaboration. However, there has been no published analysis of research collaboration networks in cardiovascular medicine using machine intelligence. Methods: Co-authorship network analysis was conducted on 2857 research articles published between 2009 and 2019. Bibliographic data were collected from the Web of Science, and the co-authorship networks were represented as undirected multigraphs. The network density, average degree, clustering coefficient, and number of communities were calculated, and the chronological changes were assessed. Thereafter, the leading authors were identified according to the centrality metrics. Finally, we investigated the significance of the characteristics of the co-authorship network in the largest component via a Monte Carlo simulation with the Barabasi-Albert model. Results: The co-authorship network of the entire period consisted of 13,979 nodes and 68,668 weighted edges. A time-series analysis revealed a linear correlation between the number of nodes and the number of edges (R-2 = 0.9937, p < 0.001). Additionally, the number of communities was linearly correlated with the number of nodes (R-2 = 0.9788, p < 0.001). The average shortest path increased by a greater degree than the logarithm of the number of nodes, indicating the scale-free structure of the network. We identified D. Berman as the most central author with regard to the degree centrality and closeness centrality. S. Neubauer was the top-ranking author with regard to the betweenness centrality. Among the 22 authors who were ranked in the top 10 for any centrality, 14 authors (63.6%) had a medical degree (medical doctor, MD). The remaining eight non-MD researchers had a PhD in computational science-related fields. The number of communities detected in the Barabasi-Albert model simulation was similar to that for the largest component of the real network (6.21 +/- 0.07 vs. 6, p = 0.096). Conclusions: A co-authorship network analysis revealed a structure of collaboration networks in the application of machine learning in the field of cardiovascular disease, which can be useful for planning future scientific collaboration.