# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 6 Datasets and Tools  
### Overview  
In GNN-based routing optimization studies, datasets typically consist of traffic matrices and network topology information from real-world networks such as Abilene, GEANT, and CERNET, which are publicly available. Each traffic matrix element reflects the amount of data transmitted between node pairs at regular intervals (e.g., every 5 to 15 min). These \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Scenario** & **Proposed Solution** & **Performance** & **Routing Policy** & **Deployment Mode** \\ \hline
[119] & 5G network & GCN + Deep Q-learning & The proposed approach achieves a lower end-to-end delay than baselines. & Delay minimization & Centralized \\ \hline
[49] & Software-defined wireless sensor network & DGRL (GCN + Actor-Critic) & DGRL can effectively reduce packet transmission delay, increase PDR, and reduce the probability of network congestion. & Delay minimization, PDR maximization, and congestion & Distributed \\ \hline
[51] & Wide area network & GNN + DRL & ENERO operates in real-world dynamic network topologies in 4.5 s on average for topologies up to 100 edges and outperforms the shortest available path heuristic baseline in terms of link utilization ratio. & Link utilization maximization & Centralized \\ \hline
[120] & SDN-based optical transport network & GNN + PPO & The introduction of evolutionary strategies helps to speed up the training time by 128 and 6 times for two network topologies, namely, NSFNET and GEANT2, respectively. & Traffic demand allocation maximization & Centralized \\ \hline
[121] & SDN Network & GCN + DDPG & The proposed strategy outperforms OSPF algorithm, DRL-TE strategy, and DDPG routing algorithm in terms of average end-to-end delay and packet loss rate. & Delay minimization and packet loss rate minimization & Centralized \\ \hline
[122] & Computer networks & GAPPO (GAT + PPO) & GAPPO outperforms benchmark algorithms with a lower packet loss ratio and a lower end-to-end delay. & Delay minimization & Centralized \\ \hline
[123] & SDN network & DGL-Routing (GCN + Actor-Critic) & The proposed scheme outperforms baselines in terms of network average end-to-end delay, packet loss rate, and throughput. & Delay minimization & Centralized \\ \hline
[124] & Computer Networks & MPDRL (MPNN + DRL) & MPDRL achieves the load balance of network traffic and improves network performance. & Network load balance & Centralized \\ \hline
[125] & Computer Networks & GCN + Multi-agent DRL & The proposed method achieves a better performance in terms of various QoS metrics & Flow set collision minimization & Distributed \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._datasets are critical for training GNN models to predict network performance under varying conditions, including different routing configurations, traffic demands, and topologies. To adapt these datasets for different network types, such as satellite or UAV networks, synthetic or simulation-generated datasets are often used, incorporating domain-specific constraints like mobility, energy limitations, and dynamic topology changes. The characteristics of these datasets, particularly their scale, traffic patterns, and topology complexity, significantly impact GNN performance. High-quality datasets with diverse topologies and traffic conditions help improve the model's generalization ability, allowing GNNs to perform well in unseen or time-varying network environments. Conversely, limited or homogeneous datasets may restrict the model's capability to handle real-world variability, leading to suboptimal routing predictions in complex networks. Benchmarking practices in GNN-based routing optimization studies typically involve comparisons with traditional routing algorithms such as [PERSON]'s shortest path algorithm, OSPF, and other heuristic-based or ML-driven techniques.