Abstract
Influential nodes are the important nodes that most efficiently control the propagation process throughout the network. Among various structural-based methods, degree centrality, k-shell decomposition, or their combination identify influential nodes with relatively low computational complexity, making them suitable for large-scale network analysis. However, these methods do not necessarily explore nodes' underlying structure and neighboring information, which poses a significant challenge for researchers in developing timely and efficient heuristics considering appropriate network characteristics. In this study, we propose a new method (IC-SNI) to measure the influential capability of the nodes. IC-SNI minimizes the loopholes of the local and global centrality and calculates the topological positional structure by considering the local and global contribution of the neighbors. Exploring the path structural information, we introduce two new measurements (connectivity strength and effective distance) to capture the structural properties among the neighboring nodes. Finally, the influential capability of a node is calculated by aggregating the structural and neighboring information of up to two-hop neighboring nodes. Evaluated on nine benchmark datasets, IC-SNI demonstrates superior performance with the highest average ranking correlation of 0.813 with the SIR simulator and a 34.1% improvement comparing state-of-the-art methods in identifying influential spreaders. The results show that IC-SNI efficiently identifies the influential spreaders in diverse real networks by accurately integrating structural and neighboring information.

Abstract
This paper contributes to developing a Method for Creating Persona Templates (MCPT), addressing a significant gap in user-centred design methodologies. Utilising qualitative data collection and analysis techniques, MCPT offers a systematic approach to developing robust and context-oriented persona templates. MCPT was created by applying the Design Science Research (DSR) methodology, and it incorporates multiple iterations for template refinement and validation among project stakeholders; all of the proposed steps of this method were based on theoretical contributions. Furthermore, MCPT was tested and refined within a real-life R&D project focusing on developing a digital platform e-marketplace for short agrifood supply chains in two iteration cycles. MCPT fills a critical void in persona research by providing detailed instructions for each step of template development. By involving the target audience, users, and project stakeholders, MCPT adds rigour to the persona creation process, enhancing the quality and relevance of personae casts. This paper contributes to the body of knowledge by offering an initial proposal of a comprehensive method for creating persona templates within diverse projects and contexts. Further research should explore MCPT's adaptability to different settings and projects, thus refining its effectiveness and extending its utility in user-centred design practices.

Abstract
Networks appear across various domains, and identifying central nodes in temporal networks is more challenging than in static networks. Temporal betweenness centrality is the widely used method to assess the importance of the nodes. This method is based on shortest temporal path calculations. However, computing this centrality metrics value is computationally intensive, especially for large-scale networks. Various approximation algorithms exist, but they often lack efficiency or accuracy. We introduce TGNN-Bet, a temporal graph neural network model, to approximate temporal betweenness centrality. In TGNN-Bet, each node gathers features from multi-hop neighbors, enabling the model to simulate paths and capture the reachability of nodes. The model's effectiveness is validated using the Spearman correlation (?) performance metric and comparing system runtimes with the existing temporal betweenness centrality method. Experimental results on six real-world temporal networks demonstrate that TGNN-Bet strongly correlates with existing temporal betweenness centrality methods. The proposed TGNN-Bet model achieves an average computation time reduction of 94.216% compared to conventional temporal betweenness centrality methods. © 2025 IEEE.

Abstract
The COVID-19 pandemic has brought new challenges to Social Solidarity Cooperatives (SSCs), affecting how they conduct their activities. The aim of this article is to analyse the extent to which SSCs’ behaviours have changed in terms of environmental practices and digital empowerment following the pandemic. Behaviour changes were assessed using a quantitative, exploratory methodology based on a questionnaire survey of 80 SSCs in Portugal. The results were analysed using a range of techniques, including descriptive analysis, exploratory factor analysis and cluster analysis. The data analysis process made it possible to group the SSCs into three distinct groups, characterised by different changes in behaviour: (i) a group of organisations with some changes in the organisation’s practices, which are more environmentally sustainable; (ii) a group of organisations that show some changes in terms of the digital transition; and (iii) a third group where there are simultaneously, and more significantly, changes in practices in terms of environmental sustainability and the digital transition. This last group is the one with the largest number of organisations in the sample. The formation of clusters is influenced by the age of the organisation and its location. © 2025, Faculty of Legal Sciences and Labor, University of Vigo. All rights reserved.

Abstract
The growing use of machine learning for integrity assessments in public administration has intensified interest in understanding how algorithms can detect corruption risk-a topic of increasing relevance in the context of rising demands for transparency. Previous research on fraud detection often overlooks the dual challenge of extreme class imbalance and the need for model explainability. This study addresses both issues by combining data-level and algorithm-level techniques in a real-world dataset from Brazil's Federal District, where there is one corruption case for every 707 non-corruption cases (a ratio of 1:707). Data engineering was essential, encompassing gathering, cleaning, transformation, and dimensionality reduction to enhance model performance and interpretability. Among the tested models, weighted logistic regression stood out, achieving the best AUC (0.692). To increase transparency, we employed SHapley Additive exPlanations, enabling both global and local interpretability of predictions. The analysis identified strong predictors of corruption risk, such as business ownership, political candidacy, and frequent job function changes. This work provides a replicable pipeline that integrates imbalanced learning and explainable AI, offering valuable contributions to risk management and decision-making in the public sector.

Abstract
In uplift modeling, the goal is to identify high-value customers based on persuadable customers, those who make a purchase only if contacted. To achieve this, uplift modeling combines machine learning techniques with causal inference, allowing businesses to refine their customer targeting strategies and focus efforts where they are most profitable. This study proposes a practical and reproducible two-phase procedure for identifying high-value customers. In the first phase, customers are segmented using decision trees, which offer a transparent and data-driven approach to grouping individuals with similar characteristics. This segmentation lays the groundwork for a meaningful interpretation of customer behavior. In the second phase, uplift is calculated for each customer segment by comparing the outcomes of the treatment and control groups. This enables the identification of customer groups with the highest uplift. A real-world use case further illustrates the value and applicability of the proposed method. To validate model performance, the procedure employs established metrics such as the Qini index and Cohen's kappa, which provide insights into both the effectiveness and reliability of the uplift estimates. This work presents a decoupled procedure for uplift modeling that leverages well-established libraries, fostering transparency and a clear understanding of the analytical process. A key contribution to uplift modeling and causal inference is the use of decision trees for stratification, which enables the creation of meaningful segments and their evaluation through the average treatment effect. By integrating theory with practical implementation, this work offers a comprehensive framework for uplift modeling that bridges academic rigor and business usability. © 2025 Elsevier B.V., All rights reserved.