Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. Early works using GNN have demonstrated an unprecedented capability to learn from different network characteristics that are fundamentally represented as graphs, such as the topology, the routing configuration, or the traffic that flows along a series of nodes in the network. In contrast to previous solutions based on Machine Learning (ML), GNN enables to produce accurate predictions even in other networks unseen during the training phase. Nowadays, GNN is a hot topic in the Machine Learning field and, as such, we are witnessing great efforts to leverage its potential in many different fields (e.g., chemistry, physics, social networks). In this context, the Graph Neural Networking challenge 2021 brings a practical limitation of existing GNN-based solutions for networking: the lack of generalization to larger networks. This paper approaches the scalability problem by presenting a GNN-based solution that can effectively scale to larger networks including higher link capacities and aggregated traffic on links.

翻译：利用GNN的早期工程展示了前所未有的能力,能够从各种网络特征中学习,这些特征基本上以图表的形式体现,例如地形、路由配置或网络中一系列节点的流量。与以前基于机械学习(ML)的解决方案相比,GNN能够提供准确的预测,即使在培训阶段的其他网络中也看不到。如今,GNN是机器学习领域的一个热门话题,因此,我们目睹了在很多不同领域(例如化学、物理、社会网络)利用其潜力的巨大努力。在这方面,“图形神经网络”挑战2021对现有的GNN的联网解决方案提出了实际限制:缺乏对大型网络的普及化。这份文件通过向更大的网络展示一个能够有效扩展到更大网络的GNN的解决方案,包括更高的连接能力和对链接的汇总流量,从而解决了可扩展性问题。