We use modern tools from network analysis and graph theory to extract patterns in the scientific ecosystem moving beyond impact indicators based on citation counts. Moreover, we also investigate how the recent rise of Large Language Models (LLMs) influences academic writing and the scientific progress.
Graph neural networks are a neural network architecture specifically designated to handle graph-type data. Different from traditional neural networks, GNNs, fall under the larger umbrella of Geometric Deep Learning which focuses on leveraging geometric information about the data to improve the network's predictions.
Large Language Models have revolutionized natural language understanding and generation, driving scientific research forward by assisting in all steps of the scientific process. We explore the applications of LLMs across various scientific domains, from the science of science to implementations in applied fields.