# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 1 Introduction
In this study, the grouping of relevant literature on GNN-based routing optimization was primarily based on three criteria: the type of learning approach employed, the specific routing objectives addressed, and the characteristics of the network scenarios examined. The studies were categorized into three main groups: supervised learning for network \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Article** & **Year** & **Summary** & **Shortcoming** \\ \hline
[23] & 2022 & Energy-efficient routing protocols for wireless sensor networks. & GNN-based solutions are not mentioned. \\ \hline
[21] & 2022 & Dynamic routing schemes in satellite networks. & GNN-based solutions are not mentioned. \\ \hline
[10] & 2022 & Machine learning-based intelligent routing algorithms. & The discussion for GNN-based solutions is not enough. \\ \hline
[34] & 2022 & Graph-based solutions for resource allocation in integrated space and terrestrial communications. & Routing optimization is not mentioned. \\ \hline
[19] & 2022 & A brief tutorial on GNNs and potential applications to communication networks, and two example use cases in wired and wireless networks. & The discussion for routing optimization is not enough. \\ \hline
[28] & 2022 & The application of graph-based deep learning methods in wireless, wired and software-defined networks. & The discussion for routing optimization is not thorough. \\ \hline
[37] & 2023 & Routing protocols in unmanned aerial vehicular networks. & GNN-based solutions are not mentioned. \\ \hline
[38] & 2023 & Routing protocols in vehicular adhoc networks. & GNN-based solutions are not mentioned. \\ \hline
[39] & 2023 & Reinforcement-learning-based routing algorithms in IoT. & GNN-based solutions are not mentioned. \\ \hline
[40] & 2024 & Machine learning solutions in IoT-based wireless sensor network routing. & GNN-based solutions are not mentioned. \\ \hline
[41] & 2024 & Routing algorithms in wireless sensor networks. & GNN-based solutions are not mentioned. \\ \hline
[42] & 2024 & Routing techniques for distributed cognitive radio networks. & GNN-based solutions are not mentioned. \\ \hline
[43] & 2024 & Routing and load-balancing mechanisms for software-defined vehicular networks. & GNN-based solutions are not mentioned. \\ \hline This survey & 2024 & The application of graph-based deep learning methods for routing optimization in a wide range of communication and networking domains. & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._modeling, supervised learning for routing optimization, and reinforcement learning for routing optimization. This classification allows for a structured understanding of how different GNN methodologies are applied across various contexts and objectives [45]. Besides these three main categories, semi-supervised network modeling and routing optimization tasks are also mentioned in the literature, with only a few studies. For example, a semi-supervised learning approach with graph convolutional networks is proposed to estimate communication delays between node pairs in large-scale communication networks [46; 47]. Considering the limited number of relevant studies, semi-supervised learning is not listed as a primary category in this paper. The main objective of this survey is to provide an overview of the promising aspects of GNN-based solutions for routing optimization, over traditional and ML techniques. Moreover, a systematic guideline for applying GNNs for routing optimization and a summary of learned lessons and research opportunities for future research can be found in this survey. The specific contributions of this survey are summarized as follows. The novelty of this survey is that it is the first survey of GNNs for routing optimization to the best of our knowledge. This survey answers the research question of whether GNN-based routing optimization is more effective than traditional solutions and the answer is yes.