### Existing Reviews  
To this end, several existing surveys have been dedicated to reviewing the various applications, development, and deployment of Digital twins (DTs) and GNNs within next-generation networks. However, these surveys typically address the two topics separately.  
On one hand, [PERSON] et al. [5] reviewed several combinatorial optimization problems on graphs while having a special eye on the applications in the telecommunications domain. The combinatorial optimization problems discussed in the paper include tasks such as network design, routing, bandwidth allocation, scheduling, and network reliability. While this survey does not explicitly focus on GNNs, a majority of the ML-based approaches to combinatorial problems, and telecommunications problems as well, are GNNs. Also, [PERSON] et al. [6] conducted a brief review of the existing literature on deep graph generation from various emerging methods to its wide application areas. [PERSON] et al. [7] provided a review of GNN focused on wireless networks. This work provides a comprehensive survey of the literature of GNNs and their application in wireless networks such as resource allocation, channel estimation, traffic prediction, and so on. Another closely related work, [PERSON] [4], proposed, for the first time, a comprehensive survey of the huge body of research leveraging various graph-based deep learning models for solving problems in different types of communication networks including wireless, wired, and software-defined networks. In the same direction, [PERSON] et al. [8] presented a brief overview of GNN for communication networks including wired and wireless networks. Furthermore, for technical issues in designing and implementing specific GNNs, various works surveyed them from the perspectives of explainability [9], dynamics [10], and hardware-software accelerators [11]. In addition, there are several overviews on applying GNN to solve problems in the IoT [12] and traffic forecasting in urban rail transit systems [13].  
On the other hand, several valuable contributions have been made in recent literature on DTs for networking systems. For instance, [PERSON] et al. [14] presented briefly how digital twins could be a powerful tool to fulfill the potential of 5G networks and beyond. [PERSON] et al. [15] conducted a survey on DT networks, which exploit DT technology to simulate and predict network dynamics, as well as enhance and optimize network management. The survey provides a comprehensive review of DT networks, covering key features, technical challenges, and potential applications in detail. In the comprehensive survey by [PERSON] et al. [3], the main contributions can be divided into two parts. In the first part, the survey focuses on reviewing potential use cases and scenarios in a 6G communication network where a DT could play an essential role. The second part focuses on reviewing the activities of the Standards Development Organization on NDTs including a description of key elements, requirements, and reference architecture. [PERSON] et al. [16] presented a taxonomy including twins for wireless and vice-versa. Moreover, [PERSON] et al. [1] introduced the general architecture of the NDT and provided some potential use cases. Two perspectives of NDTs were briefly discussed including leveraging GNNs for the core components of NDTs and coupling a network optimizer with the NDT. Quantitative results for routing optimization are provided as examples of NDTs. Furthermore, [PERSON] et al. [17] conducted an extensive review of the state-of-the-art research results on blockchain-based DT, highlighting their key benefits.  
However, to the best of our knowledge, no survey has thoroughly investigated the GNN-based DTs for networks. This gap in the existing literature highlights the need for further exploration and research in this area. We summarize the comparison between related works and our paper in Table 1.