# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 1 Introduction  
Graph Neural Networks (GNNs) have brought about a revolutionary change in fields that deal with structured data represented as graphs, particularly in computer networking, where its adoption has been rapid due to its effectiveness in various use cases such as performance modeling, routing optimization, and resource allocation [1]. GNNs have garnered significant attention in the machine learning field due to their ability to produce accurate predictions even for networks not present in the training data, making them a subject of active research and interest. Moreover, GNNs are being explored in different domains such as chemistry, physics, and social networks. Although Neural Networks (NNs) have proven to be excellent function approximators for structured data, they are not suitable for learning from data with complex relationships such as graph data [2, 3]. In networking, nodes of the graph data depict networking entities with their features, and edges are the connections or relationships between the different entities. These connections can represent various types of interactions or data flows, encapsulating the intricate and non-euclidean structure inherent to network systems. Thus, a more sophisticated and specialized neural network architecture is required to handle the graph data effectively, leveraging the rich representational capacity of graphs to model the complex, non-linear interdependencies between entities in a network. Thus, GNNs are being employed innetworking domains to accurately estimate performance by learning from graph features and structures. Selecting the optimal route while simultaneously ensuring Quality of Service (QoS) for network flows is a complex task, particularly as network resources can dynamically change. Traditional networks, with limitations such as per-hop decision making and challenges in applying flow-based QoS due to a lack of a global network view, face ongoing issues in routing [4]. QoS-driven routing emerges as a solution, providing strategies that identify the best paths to meet the QoS requirements of a maximum number of flows. Specifically focusing on minimizing delays, QoS-driven routing becomes crucial for maintaining flows within guaranteed QoS routes and achieving optimized Quality of Service. For this purpose, accurate prediction of QoS, using models such as RouteNet is imperative. RouteNet is a novel GNN model that has shown its effectiveness in producing accurate estimations of per-source/destination pair mean delay and jitter in networks with comparable accuracy to resource-intensive packet-level simulators. Its specialized GNN architecture operates on data structured as graphs, enabling it to learn and model the intricate relationships among network topology, routing, and input traffic. This enables RouteNet to make performance predictions even in scenarios that were not observed during the training phase [5]. RouteNet has the potential to address the limitations of existing GNN models, particularly in generalizing to larger graphs, making it a promising solution for various network modeling and optimization tasks. Existing GNNs in networking applications struggle to adapt effectively to more complex network structures limiting their practical use in diverse real-world scenarios. These limitations restrict the development and adoption of accurate and scalable network models in various networking applications, including performance modeling, routing optimization, and resource allocation in wireless networks [2]. The primary limitation of RouteNet lies in its diminished accuracy when faced with evaluation data distributions divergent from the training set, and it does not generalize well to networks considerably larger than those encountered during the training phase [6]. To address this issue, exploring alternative and innovative approaches for efficiently training GNN-based models to handle networks with varying complexities is essential.