## 3 Graph Neural Networks for Next-Generation Networks
### GNN in Core Networks
#### 3.3.1 Resource Allocation
[97] address the limitations of previous VNE approaches by introducing GraphViNE, a parallelizable VNE solution based on spatial GNNs. By incorporating server clustering, GraphViNE improves runtime and performance. Simulation experiments demonstrate that GraphViNE reduces runtime by a factor of eight and achieves an approximately 18% improvement in the revenue-to-cost ratio compared to other algorithms. In a similar direction, [PERSON] et al. [98] propose a novel VNE algorithm that integrates RL and GNNs. This algorithm leverages a self-defined fitness matrix and fitness value to establish an objective function, enabling the efficient implementation of a dynamic VNE algorithm and effectively reducing resource fragmentation. The proposed method is evaluated using comparison algorithms, and simulation experiments validate its effectiveness. The results demonstrate that the dynamic VNE algorithm, which combines RL and GNNs, exhibits favorable VNE characteristics. Additionally, by modifying the resource attributes of both the physical and virtual networks, the algorithm demonstrates its flexibility in adapting to different network configurations. Software-defined network (SDN) and Network function virtualization (NFV) have significantly advanced software-based control technology and reduced costs. SFC plays a crucial role in efficiently routing traffic through network servers to process the requested NFV while maintaining Service-Level Agreements (SLAs). However, SFC faces challenges in maintaining high QoS in complex scenarios. Existing approaches, such as deep neural networks (DNNs) [101], lack efficiency in utilizing network topology information and cannot handle dynamically changing topologies. To address these limitations, [PERSON] et al. [99] propose a novel SFC model based on GNNs that leverages the graph-structured characteristics of network topology. Comprising an encoder and a decoder, the model functions by having the encoder capture the representation of the network topology, while the decoder estimates probabilities for neighboring nodes to handle a VNF. Experimental results demonstrate that the proposed GNN-based architecture outperforms previous DNN-based baseline models. Furthermore, the GNN-based model offers flexibility in adapting to new network topologies without requiring redesigning and retraining. Figure 6: A calculation example of GNN [96]. With the increasing demand for dynamic SFCs and the growing sensitivity of service providers towards energy consumption in NFV, there is a need to address the Energy-efficient Graph-structured SFC problem (EG-SFC). [PERSON] et al. [100] recognize this problem and formulate it as a combinatorial optimization problem. To tackle EG-SFC, the authors propose an E2E GNN based on the constrained DRL approach. Their method utilizes GCNs to represent the Q-network of the Double Deep Q-Network (DDQN) [102] within the DRL framework. Additionally, the authors introduce a masking mechanism to mask the servers with insufficient remaining resources by assigning them with large negative values so that they are completely ignored from the deployment of new VNFs. This is expected to accelerate the training process. Experimental results demonstrate the effectiveness of the proposed method in handling unseen SFC graphs. It outperforms the least delay greedy (LDG) algorithms and traditional DDQN approaches, showcasing its ability to achieve better performance in terms of energy efficiency.