# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 1 Introduction
As an extension of artificial neural networks on graph data, graph neural networks (GNNs) are seen as an opportunity to address the existing limitations and design new routing paradigms for future networks [12; 13]. GNNs are a type of deep learning model designed to work with data that can be represented as graphs, such as social networks, biological networks, and recommendation systems. Unlike traditional neural networks that operate on grid-like data such as images, GNNs can directly process and learn from the complex relationships and structures inherent in graphs [14]. GNNs consist of layers of neural network units that aggregate and propagate information across the nodes and edges of a graph [15]. Through iterative message passing and aggregation steps, GNNs can capture both local and global patterns in the graph data, enabling tasks such as node classification, link prediction, and graph-level prediction [16]. These models have shown promising results in various domains where data are naturally represented as graphs, offering powerful tools for learning from interconnected data [17]. Traditional routing algorithms and protocols, such as [PERSON]'s algorithm and OSPF, typically rely on predefined heuristics and static optimization strategies that excel in stable and predictable network environments. However, they struggle to adapt to the dynamic and complex nature of modern communication networks, where conditions can change rapidly due to factors like traffic fluctuations, mobility, and varying topology. In contrast, GNNs offer a more flexible and robust approach by leveraging their ability to learn from graph-structured data, capturing intricate relationships between nodes (e.g.,network devices) and edges (e.g., network connections) [18]. GNNs excel in their capacity for generalization, allowing them to adapt to unseen topologies and conditions that were not part of the training data. This capability is particularly beneficial in next-generation communication networks, such as 5G and beyond, where high mobility and the need for real-time decision-making are paramount. Furthermore, GNNs can effectively model complex dependencies within the network, leading to improved routing decisions that optimize performance metrics such as latency, throughput, and energy efficiency. By employing iterative message-passing mechanisms, GNNs can continuously refine their predictions based on real-time data, making them a compelling choice for dynamic and scalable routing in the evolving landscape of communication networks [19]. Some surveys have focused on the routing algorithms and protocols in specific network scenarios. Routing algorithms for mobile ad hoc networks (MANETs) are classified into four categories in [20], namely, performance improvement, QoS-aware, energy-saving, and security-aware, with different optimization objectives. Both single-layer and multi-layer dynamic routing schemes in satellite networks are reviewed in [21], including software-defined networking (SDN)-based, QoS-based, and traffic-balancing dynamic routing. Considering the energy constraints in wireless sensor networks (WSNs), the selection of a cluster head has been an important factor when designing energy-efficient routing protocols in WSNs [22]. Both classical low-energy adaptive clustering hierarchy (LEACH) and bio-inspired protocols are reviewed in [23], with a focus on the criteria of cluster head selection. The introduction of SDN has provided new opportunities for many applications in the networking domain, including routing [24]. SDN has a three-layer architecture: management, control, and data planes. By separating these planes, SDN empowers networking with strong control, programmability, and automation, adding new features to solve classical problems in traditional networks [25]. Some surveys have discussed routing algorithms and protocols for SDN [25; 26].