# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 1 Introduction
The topic of GNN-based routing optimization, as explored in the paper, resonates with broader advancements in network management, artificial intelligence, and graph-based machine learning techniques [35; 36]. Recent works on DRL for network optimization, for instance, focus on adaptive and real-time decision-making in areas such as load balancing, resource allocation, and traffic prediction. While DRL models excel at real-time routing decisions, GNNs provide a distinct advantage by capturing complex topological dependencies that are vital in networks with dynamic or irregular structures, such as UAV and satellite networks. Furthermore, the paper aligns with emerging trends in intent-based networking (IBN), where AI models predict and enforce network policies to meet high-level business or performance goals. GNNs, with their ability to generalize over unseen topologies, could further enhance the predictive capabilities of IBN by providing more robust and scalable solutions. Similarly, research on SDN and network function virtualization (NFV) intersects with GNN-based routing as both fields aim to make networks more programmable and efficient. These synergies suggest that GNN-based routing optimization could have broader applications in next-generation network architectures, making it a powerful tool not just for routing but for holistic network automation and intelligent infrastructure management. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Year** & **Summary** & **Shortcoming** \\ \hline
[25] & 2021 & Supervised learning, unsupervised learning, and reinforcement learning techniques in SDN. & GNN-based solutions are not mentioned. \\ \hline
[11] & 2021 & RL-based routing protocols for vehicular ad hoc networks. & GNN-based solutions are not mentioned. \\ \hline
[1] & 2021 & ML-based routing optimization techniques for future networks. & The discussion for GNN-based solutions is not thorough. \\ \hline
[7] & 2022 & AI-enabled routing protocols for UAV networks. & GNN-based solutions are not mentioned. \\ \hline
[8] & 2022 & Q-learning-based position-aware routing protocols for FANETs. & GNN-based solutions are not mentioned. \\ \hline
[20] & 2022 & Routing algorithms for MANETs with performance improvement, QoS-aware, energy-saving, and security-aware categories. & GNN-based solutions are not mentioned. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of this survey and existing surveys. The process of selecting and evaluating relevant papers involved a comprehensive literature search using platforms like Google Scholar, employing keywords such as \"Routing\", \"Network Modeling\", \"Graph Neural Network\", and \"Graph Convolutional Network\". Additional criteria included the reputation of the journals or conferences, relevance to the study's objectives, and the quality of the papers, which was assessed through manual checks of methodologies and results. Specific metrics and measurements considered during the review included accuracy metrics such as mean relative error (MRE) and mean absolute percentage error (MAPE), as well as performance indicators related to latency, throughput, packet delivery ratio (PDR), and energy efficiency [44]. These metrics provide insight into the effectiveness of various GNN models in optimizing routing decisions under different network conditions. By systematically evaluating these studies against established criteria, the review aimed to present a clear overview of the current state of research in GNN-based routing optimization, highlighting both the advancements made and the challenges that remain. In summary, 36 studies are included in this survey, including 16 journal publications, 17 conference publications, and three preprint papers; the first relevant study was published in 2018.