The use of Generative Artificial Intelligence (GenAI) is revolutionising how companies create digital content on social networks. However, how users respond to this content remains less explored. The aim of this article is to compare GenAI-generated content with human-generated content on Instagram. First, through a survey of 273 students, we explored user engagement with the eSports team’s content on Instagram. Second, we measured followers’ engagement behaviors (i.e., “likes”, comments, shares and reach) with GenAI-generated content versus human-created content. Results indicate that both GenAI tools (ChatGPT and Gemini) achieved similar levels of engagement in terms of “likes”. ChatGPT stood out for its use of interactive features like opinion polls, particularly in “stories”, while Gemini excelled in total reach and visibility. In contrast, human-generated content attracted a higher proportion of non-followers, suggesting a stronger potential to expand the existing audience. These findings underscore the advantages of adopting a hybrid strategy that combines the scalability and speed of GenAI with the contextual relevance and authenticity of human-created content.

To identify influential nodes in real networks, it is essential to note the importance of considering the local and global information in a network. In addition, it is also key to consider the dynamic information. Accordingly, the main aim of this paper is to present a new centrality measure based on return random walk and the effective distance gravity model (CRRWG). This new metric increases the relevance of nodes with a dual role: i) at the local level, they are important in their community or cluster, and ii) at the global level, they give cohesion to the network. It has advantages over other traditional models of centrality since it considers the global and local information, as well as the information of the dynamic interaction between the nodes, as recent studies on community-aware centrality measures demonstrate. Thus, the combination of dynamic and static information makes it easier to detect influential nodes in complex networks. To validate the effectiveness of the proposed centrality measure, it is compared with classic measures, such as Degree, Closeness, Betweenness, PageRank, and other measures based on the gravity model, effective distance and community-aware approaches. The experimental results show the effectiveness of CRRWG through a set of experiments on different types of networks.

Pollution in urban areas is turning into a primary focus for local governments in developed nations around the globe. Lots of data are currently collected for this from smart developments provided with atmospheric and climatic sensors. A hot research line is now exploiting such data to extract patterns and predict pollution levels in such a way that countermeasures can be taken beforehand and exposure to harmful concentrations is avoided. However, a key issue is the lack of significant data, due to incomplete smart infrastructures or calibration problems in sensors. Dealing with this, in this paper we propose the exploitation of synthetic data generation to enhance pollution prediction based on limited data sources, concretely extending real measurements of two weeks to up to ten extra years. We present a data generation approach based on Generative Adversarial Networks (GANs), with a particular model focused on generating artificial pollution data, which is later exploited using different Machine Learning (ML) algorithms. Results indicate that the usage of synthetic data further improves prediction when used as the basis dataset to be later finetuned using real records. For 62% of pollutants this way to proceed in data mixing (among five different approaches) provides the best results in evaluations. Such effect is due to extra model robustness due to data regularization, and better generalization capabilities by avoiding sensor limitations in real deployments.

The World Health Organization highlights that the number of annual road traffic deaths has reached 1.35 million (Global Status Report on Road Safety 2018). In addition, million of people suffer more or less important injuries as a consequence of this type of accidents. In this scenario, the prediction of the severity of traffic accidents is an essential point when it comes to improving the prevention and reaction of the entities responsible. On the other hand, the development of reliable methodologies to predict and classify the level of severity of traffic accidents, based on various variables, is a key component in the field of research in road safety. This work aims to propose a new approach, based on convolutional neural networks, for the detection of the severity of traffic accidents. Behind this objective is the preprocessing, analysis and visualization of data as well as the design, implementation and comparison of machine learning models considering accuracy as a performance indicator. For this purpose, a scalable and easily reusable methodology has been implemented. This methodology has been compared with other deep learning models verifying that the results of the designed neural network offer better performance in terms of quality measures.

Motivated by the large number of wearables offering geolocation, human mobility mining has emerged as an novel research field within AI. The study of mobility creates increasingly predictable models in which it is easy to find patterns of behaviour. However, this data is not publicly available and access to it is restricted to large telecommunications operators. In this context, this paper aims to solve one of the main problems of human mobility databases, i.e. the scarcity of data for the generation of human mobility models. For this purpose, Generative adversarial network (GANs) have been proposed to generate synthetic time-series mobility data. Moreover, several neural network models are proposed to assess the impact of synthetic data generation on the prediction of human mobility. Our results show that the use of synthetic data improves predictions of human mobility compared to models based on available measured data. Specifically, the reinforcement learning with synthetic data benchmark, when compared to using only ground truth data, achieved a 1.22% improvement in R2, a 0.70% reduction in RMSE, a 2.97% decrease in MAE, a 27.07% reduction in MAPE, and an 18.18% improvement in CVRMSE, demonstrating its effectiveness in enhancing predictive accuracy.Motivated by the large number of wearables offering geolocation, human mobility mining has emerged as an novel research field within AI. The study of mobility creates increasingly predictable models in which it is easy to find patterns of behaviour. However, this data is not publicly available and access to it is restricted to large telecommunications operators. In this context, this paper aims to solve one of the main problems of human mobility databases, i.e. the scarcity of data for the generation of human mobility models. For this purpose, Generative adversarial network (GANs) have been proposed to generate synthetic time-series mobility data. Moreover, several neural network models are proposed to assess the impact of synthetic data generation on the prediction of human mobility. Our results show that the use of synthetic data improves predictions of human mobility compared to models based on available measured data. Specifically, the reinforcement learning with synthetic data benchmark, when compared to using only ground truth data, achieved a 1.22% improvement in R2, a 0.70% reduction in RMSE, a 2.97% decrease in MAE, a 27.07% reduction in MAPE, and an 18.18% improvement in CVRMSE, demonstrating its effectiveness in enhancing predictive accuracy.

Centrality metrics are one of the most meaningful features in a large number of real-world network systems. In that sense, the Betweenness centrality is a widely used measurement that quantifies the importance of a node in the information flow in a network. Moreover, there is a centrality measure, based on random-paths betweenness centrality, that provides a classification of the nodes of undirected networks, that are able to reinforce dense communities according to their role. In this paper, a new centrality model, based on random-paths betweenness centrality and applied on directed networks, is presented. This model, based on four indices, describes the behaviour of the nodes within the network in terms of its role, such as a transition node, in the same cluster or between clusters. Finally, we evaluate the model with several use cases based on real networks, two of them are proposed and created in this paper, giving insight into some interesting findings about the networks’ features.

Key nodes have a significant impact, both structural and functional, on complex networks. Evaluating the importance of a node, from an information flow perspective, has applications in many areas but, to a great extent, they are focused on the shortest path perspective. Then, there exist measures based on the representation of the frequency in which each node plays the role of a transition node by random trajectories between two other nodes. Development of a specific kind of material, metallic glasses, has been mainly based on trial and error methods and, this approach is inefficient in terms of time and cost. Therefore, the development of new approaches for metallic glasses design remains an open topic for researchers in this field. The understanding of the role of each element could be useful for the generation of new metallic glasses and the improvement of existing ones. This work aims to study the formation capacity of metallic glass from the point of view of complex networks, using random walk betweenness centrality approaches, as well as the proposal of a new hypothesis that can serve as a roadmap for future research in this field of generation of new metallic glass. The results and discussions indicate that the proposed approach may be of great interest, for the development of metallic glasses, given the great correlation between the results of this study and the actual obtaining of metallic glasses in recent research.