Evolution of research in biomedical sciences - a network-based characterization based on PubMed

The rapid growth of scientific publications every year makes it infeasible to keep pace with and survey manually, even for a specific field. Keeping up with literature and gaining a birds-eye view in a timely manner is crucial to the pursuit of scientific discovery and innovation. To help gain a clearer understanding of the state and progress of science and the nature of discovery, one can encode key information from these publications and represent them as a network. Observations on the structural evolution of these graphs can offer valuable insights on the dynamics at play. This work describes the construction and analyses the temporal evolution of a knowledge network of keywords (specifically focusing on genes/proteins, diseases and chemicals) from publications in the biomedical sciences domain. We compare and contrast the representations and evolution of these keyword networks types and find significant differences in the network growth, largely corresponding to our intuition. Furthermore, we focus on the formation and evolution of new links, which we argue corresponds to new scientific discoveries. Our findings suggest that these links are progressively formed in short network distance, leading to clusters of extensively studied keywords. This strategy, however, seems to impede ground-breaking innovation, which could be beneficial for research progress.