## 2 Digital Twins for Next-Generation Networks  
Digital twins (DTs) emerged in the early 2000s when [PERSON] introduced them during a course presentation on product lifecycle management. By 2011, implementing DTs was regarded as a challenging process that demanded advancements in various technologies. Although the term \"digital twin\" emerged in 2003, NASA provided the first detailed account of its application in Technology Roadmaps several years later. In this context, a twin was employed to replicate space conditions and conduct tests for flight preparation. Initially rooted in the aerospace sector, the adoption of DTs expanded to the manufacturing industry around 2012. Other than the three main components, the digital (virtual part), the real physical product, and the connection between them, according to [PERSON] et al. [18], a DT can be extended to five components by including data and service as a part of it. While sharing a similar purpose with simulations, a digital twin is expected to be more potent by adopting a data-driven approach to model physical objects. Furthermore, the bidirectional synchronization in the connection allows for the real-time updating of the digital replica in response to changes in the mirrored physical object and facilitates the monitoring of the associated product through a virtualization layer. Simulations, on the other hand, focus on modeling the physical entity in specific scenarios or time frames. At the simplest level, a DT is a digital counterpart of a single, atomic, physical thing/system. In a web-based context, at this stage, the DT is a mere proxy of an IoT device with eventually augmented capabilities. However, the digital twin concept can be applied to larger systems in a cascading manner [19], where it can be considered as Things in the Web Of Things vision: they are components of a larger graph of Things and can be composed in a bottom-up or top-down fashion to realize large-scale cyber-physical systems in different application domains, e.g., transportation networks [20; 21; 22], water distribution networks [23], smart manufacturing systems [24], etc. In this context, Graph neural networks (GNNs) can be a relevant tool that can help in building additional AI-based services on top of this graph of DTs. Figure 1: The structure of our paper. The application of the digital twin paradigm to network management and operation fits into the continuation of global network digitization. It is seen as a high-value target by network operators, and thus there are parallel efforts to offer a standardized definition of what is a Network DT, i.e., DTN or NDT. One can be found in the International Telecommunication Union (ITU) recommendation document [25]: \"_A digital twin network (DTN) is a virtual representation of the physical network. DTN is useful for analyzing, diagnosing, emulating, and controlling the physical network based on data, model, and interface, to achieve a real-time interactive mapping between physical networks and virtual twin networks. According to the definition, DTN contains four key characteristics: data, mapping, model, and interface [...]\({}^{\prime\prime}\)._ ITU also proposes a reference architecture of a Network digital twin (NDT) (see Figure 2), including three layers, three domains, and a double closed-loop. The three layers consist of the physical network layer, the Network digital twin (NDT) layer, and the network application layer. Inside the NDT layer, three domains are defined corresponding to three key subsystems: unified data repository, unified data models, and DT entity management. While the unified data repository serves as a single source of data for this layer, and provides the capability to collect, store, serve, and manage data, the unified data model is the ability source of this layer, equipped with specific model instances for different network applications.