# DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network
## 2 Related Works  
This section describes how DTL-GNN utilizes Attribute Adjacency Graph (AAG) and complex network graph data modeling and provides a brief overview of the current lightweight approaches in digital twins. With the continuous and rapid development of enabling technologies such as neural networks [22], digital twin systems are increasingly integrating advanced technologies from various interdisciplinary fields [23]. These technologies are also widely applied in the research on the lightweight of digital twin systems. Existing research on lightweight digital twins mainly focuses on two directions [24]. The first category targets computational process optimization during data interaction, often using methods like distributed computing, federated learning, and surrogate model optimization to reduce computational complexity or decentralize the computation process [25]. For example, [PERSON] et al. propose a lightweight scheme for air-ground systems [26], which allocates the computational burden to other devices through a distributed computing method to achieve lightweight on a particular device without simplifying the digital twin system at its source. The second method category focuses on lightweight in virtual space. [PERSON] et al. proposed a method for sharing virtual entities but did not involve simplification of the model itself [27]. [PERSON] et al. proposed a simplification method based on quadratic error measurement without hierarchical simplification of the model [28]. [PERSON] et al. proposed a manufacturing execution system (MES) simulation framework tailored for small and medium-sized production enterprises. However, the approach of constructing a rapid factory model based on the smallest work section unit lacks fine-grained model classification [29]. [PERSON] et al. designed a lightweight model-driven digital twin using professional knowledge without considering details [15]. [PERSON] et al. proposed a lightweight digital twin method for transportation systems that directly discards the specific class of models [14]. However, in certain cases, it is essential to retain specific shape features to accurately reflect changes in dynamic processes. The existing second-category feature recognition methods are relatively limited, primarily focusing on the identification of component features, topological relationships, and assembly networks, but they are unable to simplify virtual entities at a fine-grained level. These methods typically employ a uniform simplification strategy, such as directly discarding models of certain categories [14] or relying on basic error metrics [28], both of which present notable limitations in preserving model details and capturing dynamic processes. This work primarily addresses the shortcomings of existing second-category methods by integrating geometric and topological data to adopt more refined simplification strategies. Graphs, as an effective mathematical tool for precisely representing relationships between objects, are extensively utilized in modeling non-Euclidean spaces [30; 31]. To accurately capture the fundamental details of virtual models, DTL-GNN employs graphs as the carriers of virtual models, leveraging the corresponding complex network to extract structural hierarchy information and using the AAG to map the attributes of each component, as illustrated in Figure 1.  
A complex network refers to a network system characterized by a sophisticated topological structure. The topological structure frequently exhibits considerable non-uniformity, variability, and dynamism. Complex networks map real-world systems into a network, aiming to study both the local and global structural characteristics of each component and the principles of interaction among individuals [32]. The examination of complex networks concentrates on both the direct connections among nodes and the investigation of sophisticated structural characteristics inside the network, including small-world phenomena [33], scale-free attributes [34], and modularity [35].