To simplify, research has moved towards the use of activity time intervals to represent interactions between entities [6]. As an example, consider how we can identify emerging skills required by companies through the analysis of job posting graphs. Job postings can be represented as graphs where nodes correspond to skills, and edges connect skills that co-occur within the same posting. By aggregating job postings over time for a specific position, we can construct a temporal graph of skills. For instance, the launch of ChatGPT in November 2022 rapidly transformed the workflows of data scientists in machine learning projects, prompting companies to seek professionals with expertise in Generative AI. Figure 1 illustrates this process: on the left, a co-occurrence graph depicts skills as vertices, with edges representing their co-appearance in job postings over time. On the right, a temporal network model visualizes these interactions, showing how timelines of (entity, time-interval) pairs can provide rich insights into significant events. These timelines, highlighted in blue, capture pivotal transitions, such as the initial focus on traditional machine learning skills and the subsequent demand for Generative AI expertise, underscoring the central role of skills in this context. This approach to mapping event-driven timelines is fundamental to addressing the MinTCover problem. Despite the interpretability and effectiveness of this problem, studies on the computational and approximation complexity highlight that MinTCover is NP-hard [6; 7; 8; 9]. For this problem, some approximate and heuristic-based approaches have been proposed [6; 10; 11; 18] which have different performances based on the characteristics of network considered (e.g., density, number of timestamps, number of nodes). However, no deep learning (DL)-based approaches have been proposed in the literature, despite the proven effectiveness of these solutions in dealing with vertex cover problems. This is also due to the complexity of modeling information on a temporal knowledge graph in a representation learning fashion, since algorithms need to take into account both structural and temporal patterns. Hence, in this paper, we present a DL-based algorithm for solving the MinTCover problem with two main goals: (i) to design a solution which can compute better results with respect to approximate or heuristic based solutions, thus providing evidence of the effectiveness of DL-based approaches for CO over temporal graphs; (ii) to explore the potential of DL algorithms for the representation and embedding of temporal knowledge graphs, aiming to provide novel insights into how advanced DL techniques can capture and analyze temporal dynamics within knowledge graphs, thereby underscoring the power and  
Figure 1: Illustration of a co-occurrence graph (**left**) and a temporal network framework (**right**), highlighting the relationship between activity timelines and dynamic interactions. The co-occurrence graph shows static connections between entities (black circles) based on interactions, while the temporal network visualizes evolving interactions over time. Activity timelines (blue) demonstrate the concept of identifying minimal intervals that summarize significant temporal interactions, which is central to the MinTCover problem. In the toy example shown, the analysis of skills coming from job postings shows the initial interest of companies in traditional machine learning replaced by the subsequent demand of Generative AI expertise. versatility of these approaches in integrating evolutionary and time-sensitive information. We, thus, propose an approach based on the combination of Graph Neural Networks (GNNs), Transformer, and Pointer Networks (PNs), enabling a nuanced representation of both the structural and temporal dynamics of nodes in temporal graphs, to build an initial solution to the problem.