# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 7 Challenges and Opportunities  
### Challenges  
Based on the literature review in the above sections, some research challenges have been identified in existing studies.  
The first challenge is the scarcity of GNN-based benchmarks for routing optimization. For GNN-based network modeling, RouteNet can be considered as a benchmark and has been used in several follow-up studies. However, a widely accepted GNN benchmark is still lacking for the remaining two research paradigms. Designed with different routing policies, as shown in Tables 4 and 5, it is unfair to compare different GNN-based solutions. For most surveyed studies, the baselines are still traditional routing algorithms, for example, shortest path routing or OSPF. A GNN-based routing benchmark is essential for developing more effective GNN-based methods, particularly those with open data and source codes for replication.  
The second research challenge is the training efficiency of the DRL model, which is considered in [120] with the introduction of evolutionary strategies. This challenge occurs when the computational complexity increases exponentially with network growth. With an increase in features, the dimension of the action space in the DRL solutions becomes larger, which makes the convergence of the DRL model slow or difficult. The DRL agent requires a long training process before achieving acceptable performance, which is unrealistic for applications in real-world networks, for example, in real elastic optical networks [110].  
The third research challenge is the implementation of GNN-based solutions in practice. Most of the surveyed studies are based on network simulations, without being validated in real-world networks. Centralized routing based on the SDN assumption accounts for the majority, with only a few exceptions [49; 53; 106]. Centralized routing relies on the entire network status information to update the hidden state, which could be unavailable in practice and has a poor generalization ability for time-varying network topologies. Communication overhead is also introduced in the processes of network status collection and parameter updates, which may cause failure to guarantee QoS for time-critical applications.  
Deploying GNNs for routing in real-world networks faces several challenges, including integration with existing infrastructure, hardware limitations, and security concerns. Many current network systems rely on well-established protocols like OSPF or MPLS, which are deeply embedded in the hardware and software of routers and switches. Integrating GNN-based models requires compatibility with these legacy systems, which can be complex and costly. Furthermore, the computational demands of GNNs, especially in large-scale networks, present significant hardware challenges. Network devices may lack the necessary processing power and memory to run GNN inference in real-time, making it necessary to offload computations to centralized or edge servers, which can introduce latency and reliability issues. Security is another concern, as GNNs, like all machine learning models, can be vulnerable to adversarial attacks where small perturbations in input data (such as traffic patterns or topology information) lead to incorrect routing decisions. While GNNs can potentially improve security by learning robust patterns and detecting anomalies in network behavior, they can also exacerbate risks if not adequately protected or if their decision-making processes are opaque to network administrators. Addressing these issues requires the development of lightweight, secure GNN implementations, improved explainability, and careful integration with existing routing protocols to ensure seamless and secure deployment.