The application of Large Language Models (LLMs) in medicine represents an area of growing interest in scientific research. This study presents a quantitative review of the scientific literature aiming at analyzing emerging trends in the use of LLMs in the medical field. Through a systematic analysis of works extracted from Scopus, the study examines the temporal evolution, geographical distribution, and scientific collaborations between research institutions and nations. Furthermore, the main topics addressed in the most cited papers are identified, and the most recent and relevant reviews are explored in depth. The quantitative approach enables mapping the development of research, highlighting both opportunities and open challenges. This study presents a comprehensive analysis of research articles and review-type articles across several years, focusing on temporal, geographical, and thematic trends. The temporal analysis reveals significant shifts in research activity, including periods of increased or decreased publication output and the emergence of new areas of interest. Geographically, the results identify regions and countries with higher concentrations of publications, as well as regions experiencing growing or stagnant international collaboration. The thematic analysis highlights the key research areas addressed in the reviewed papers, tracking evolving topics and changes in research focus over time. Additionally, the collaborative analysis sheds light on key networks of international collaboration, revealing changes in the distribution of affiliations across subperiods and publication types. Finally, an investigation of the most cited papers highlights the works that have had the greatest impact on the scientific community, identifying enduring themes and methodologies that continue to shape the field of study. The results provide a clear overview of current trends and future perspectives for the application of LLMs in medicine, offering a valuable reference for researchers and professionals in the field.

Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the system itself. For instance, in social networks, where the nodes are users, links represent relationships (such as acquaintance, friendship, etc.), whereas in information spreading networks, nodes are users and content and links represent interactions, diffusion, etc. However, while many approaches involve machine learning-based algorithms, just the most recent ones account for the topology of the network, e.g., geometric deep learning techniques to learn on graphs, and most of them do not account for the temporal dynamics in the network but train on snapshots of the system at a given time. In this paper, we aim to explore Temporal Graph Networks (TGN), a Graph Representation Learning-based approach that natively supports dynamic graphs and assigns to each event (link) a timestamp. In particular, we investigate how the TGN behaves when trained under different temporal granularity or with various event aggregation techniques when learning the inductive and transductive link prediction problem on real social networks such as Twitter, Wikipedia, Yelp, and Reddit. We find that initial setup affects the temporal granularity of the data, but the impact depends on the specific social network. For instance, we note that the train batch size has a strong impact on Twitter, Wikipedia, and Yelp, while it does not matter on Reddit.

The study of the behaviors of large community of researchers and what correlations exist between their environment, such as grouping rules by law or specific institution policies, and their performance is an important topic since it affects the metrics used to evaluate the quality of the research. Moreover, in several countries, such as Italy, these metrics are also used to define the recruitment and funding policies. To effectively study these topics, we created a procedure that allow us to craft a large dataset of Italian Academic researchers, having the most important performance indices together with co-authorships information, mixing data extracted from the official list of academic researchers provided by Italian Ministry of University and Research and the Elsevier’s Scopus database. In this paper, we discuss our approach to automate the process of correct association of profiles and the mapping of publications reducing the use of computational resources. We also present the characteristics of four datasets related to specific research fields defined by the Italian Ministry of University and Research used to group the Italian researchers. Then, we present several examples of how the information extracted from these datasets can help to achieve a better understanding of the dynamics influencing scientist performances.

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.