## 3 Graph Neural Networks for Next-Generation Networks
### GNN in Core Networks
#### 3.3.1 Resource Allocation  
In this section, we discuss resource allocation within the core network, with the common problems of Virtual network embedding (VNE) and Service function chain (SFC), as summarized in Table 5.  
VNE involves the placement of virtual network services onto substrate network components. The performance of VNE algorithms plays a crucial role in determining the effectiveness and efficiency of a virtualized network, making it a critical component of network virtualization technology. To improve performance, VNE algorithms need to autonomously detect and adapt to complex and time-varying network conditions. They should dynamically provide solutions that are best suited to the current network status, considering factors such as resource availability, network topology, and service requirements. In this context, [PERSON] et al. [94] address the challenge of automatic virtual network embedding by combining DRL with a neural network structure based on GCN. They propose an algorithm that leverages a parallel RL framework and a multi-objective reward function for training. Through extensive simulations under various scenarios, the algorithm demonstrates strong performance across multiple metrics compared to existing state-of-the-art solutions. Notably, it achieves significant improvements in acceptance ratio and average revenue, with up to 39.6% and 70.6% improvements, respectively. The results also highlight the robustness of the proposed solution in handling different network conditions. Similarly, [PERSON] et al. [95] introduce _DeepOpt_, a framework where both DRL and GNN are employed to tackle the Virtual network function (VNF) placement challenge. They \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Model** & **Baseline** & **Objective** & **Metric** \\ \hline
[94] & GCN, DRL & R-ViNE, D-ViNE, GRC, MCVNE, and NodeRank & VNE & Acceptance ratio, average revenue latency, node, and link resource utility \\ \hline
[95] & GNN, DRL & DDQN, MSGAS, Eigendecomposition & VNF placement & SFC rejection ratio, computation time \\ \hline
[96] & GNN, DRL & OFM, size-greedy, pairwise, random & Reduce flow migration cost & Migration cost, computation time \\ \hline
[97] & GNN, Kmeans & FirstFit, BestFit, GRC, NeuroViNE & Improve runtime and performance & Parallelizability, acceptance ratio, revenue and cost, CPU and link utilization \\ \hline
[98] & GNN, RL & NodeRank, MCST-VNE, GCN-VNE & Dynamic VNE, reduce resource fragmentation & Revenue, acceptance rate \\ \hline
[99] & GNN, ILP & DNN-based model & SFC & Cost (delay): average, fail ratio, overmax \\ \hline
[100] & GCN, DRL & LDG, DNN-DDQN & SFC & E2E delay \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of application of GNN-based models for resource allocation in core networks. formulate the task as a sequential decision-making problem with the aim of minimizing the cost of deployment and enhancing the Quality of service (QoS) of network flows, particularly the E2E delay. They also argue that GNNs contribute to improved generalization across diverse network topologies. The simulation results indeed show that the model outperforms existing algorithms without GNN in the VNF placement task. To ensure QoS while minimizing processing resources, network operators face the challenge of efficiently migrating traffic flows among network function instances in response to unpredictable network traffic. [PERSON] et al. [96] propose _DeepMigration_, a solution that combines DRL with GNN to dynamically and accurately migrate traffic flows among network function instances (Figure 6). DeepMigration leverages the graph-based relationship deduction capability of their customized GNN and the self-evolution ability of DRL to model the cost and benefit of flow migration such as migration latency and reduction in the number of NF instances. This enables DeepMigration to generate effective and dynamic flow migration policies. [PERSON] et al.