Abstract
The aim of this chapter is to present a domain model that represents the informational needs of transparency (governance structure and accountability dimensions) in Portuguese cooperatives. A domain model is an abstract representation of a reality and a milestone in the development of a metadata application profile (MAP). A community of practice publishes linked open MAP-based data for these data to be interoperable; this means intelligent software/agents can aggregate these data, provide different types of visualizations, infer from the data, and ultimately provide new discoveries. This model was developed having as basis the information obtained from the accomplishment of a focus group, and the analysis of financial reports and websites of seven Portuguese cooperatives. The authors will continue to work on the domain model to include 1) other dimensions that also contribute for transparency in the organizations and 2) other types of entities of the social economy (SE). The final aim is to define a model representing the needs of transparency of all types of European SE entities.

Abstract
This chapter presents the early stages of a metadata application profile (MAP) development that uses a process of reverse engineering. The context of this development is the European poetry, more specifically the poetry metrics and all dimensions that exist around this context. This community of practice has a certain number of digital repertoires that store this information and that are not interoperable. This chapter presents some steps of the definition of the MAP Domain Model. It shows how the developers having as starting point these repertoires, and by means of a reverse engineering process are modeling the functional requirements of each repertoire using the use-case modeling technique and are analyzing every database logical models to extract the conceptual model of each repertoire. The final goal is to develop a common conceptual model in order to use it as basis, together with other sources of information, for the definition of the Domain Model.

Abstract

Abstract
Identifying the influential spreaders in complex networks has emerged as an important research challenge to control the spread of (mis)information or infectious diseases. Researchers have proposed many centrality measures to identify the influential nodes (spreaders) in the past few years. Still, most of them have not considered the importance of the edges in unweighted networks. To address this issue, we propose a novel centrality measure to identify the spreading ability of the Influential Spreaders using the Potential Edge Weight method (IS-PEW). Considering the connectivity structure, the ability of information exchange, and the importance of neighbouring nodes, we measure the potential edge weight. The ranking similarity of spreaders identified by IS-PEW and the baseline centrality methods are compared with the Susceptible-Infectious-Recovered (SIR) epidemic simulator using Kendall’s rank correlation. The spreading ability of the top-ranking spreaders is also compared for five different percentages of top-ranking node sets using six different real networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
This chapter presents the early stages of a metadata application profile (MAP) development that uses a process of reverse engineering. The context of this development is the European poetry, more specifically the poetry metrics and all dimensions that exist around this context. This community of practice has a certain number of digital repertoires that store this information and that are not interoperable. This chapter presents some steps of the definition of the MAP Domain Model. It shows how the developers having as starting point these repertoires, and by means of a reverse engineering process are modeling the functional requirements of each repertoire using the use-case modeling technique and are analyzing every database logical models to extract the conceptual model of each repertoire. The final goal is to develop a common conceptual model in order to use it as basis, together with other sources of information, for the definition of the Domain Model.

Abstract
The optimal Coalition Structure Generation (CSG) problem for a given set of agents finds a partition of the agent set that maximises social welfare. The CSG problem is an NP-hard optimisation problem, where the search space grows exponentially. The exact and approximation algorithms focus on finding an optimal solution or a solution within a known bound from the optimum. However, as the number of agents increases linearly, the search space increases exponentially and a practical option here is to use heuristic algorithms. Heuristic algorithms are suitable for solving the optimisation problems because of their less computational complexity. TACOS is a heuristic method for the CSG problem that finds high-quality solutions quickly using a neighbourhood search performed with a memory. However, some of the neighbourhood searches by TACOS can be performed simultaneously. Therefore, this paper proposes a parallel version of the TACOS algorithm (P-TACOS) for the CSG problem, intending to find a better solution than TACOS. We evaluated P-TACOS using eight (8) benchmark data distributions. Results show that P-TACOS achieves better results for all eight (8) data distributions. P-TACOS achieves the highest gain, 74.23%, for the Chisquare distribution and the lowest gain, 0.01%, for the Normal distribution. We also examine how often P-TACOS generates better results than TACOS. In the best case, it generates better results for 92.30% of the time (for the Rayleigh and Agent-based Normal distributions), and in the worst case, 38.46% of the time (for the Weibull distribution).