Computer networks are the foundation of modern digital infrastructure, facilitating global communication and data exchange. As demand for reliable high-bandwidth connectivity grows, advanced network modeling techniques become increasingly essential to optimize performance and predict network behavior. Traditional modeling methods, such as packet-level simulators and queueing theory, have notable limitations --either being computationally expensive or relying on restrictive assumptions that reduce accuracy. In this context, the deep learning-based RouteNet family of models has recently redefined network modeling by showing an unprecedented cost-performance trade-off. In this work, we revisit RouteNet's sophisticated design and uncover its hidden connection to Topological Deep Learning (TDL), an emerging field that models higher-order interactions beyond standard graph-based methods. We demonstrate that, although originally formulated as a heterogeneous Graph Neural Network, RouteNet serves as the first instantiation of a new form of TDL. More specifically, this paper presents OrdGCCN, a novel TDL framework that introduces the notion of ordered neighbors in arbitrary discrete topological spaces, and shows that RouteNet's architecture can be naturally described as an ordered topological neural network. To the best of our knowledge, this marks the first successful real-world application of state-of-the-art TDL principles --which we confirm through extensive testbed experiments--, laying the foundation for the next generation of ordered TDL-driven applications.

翻译：计算机网络是现代数字基础设施的基石，支撑着全球通信与数据交换。随着对可靠高带宽连接需求的增长，先进的网络建模技术对于优化性能和预测网络行为变得日益关键。传统的建模方法，如分组级模拟器和排队论，存在显著局限性——要么计算成本高昂，要么依赖限制性假设而降低准确性。在此背景下，基于深度学习的RouteNet系列模型通过展现出前所未有的成本-性能权衡，近期重新定义了网络建模范式。本工作中，我们重新审视RouteNet的复杂设计，揭示了其与拓扑深度学习这一新兴领域的潜在关联——该领域旨在建模超越标准图方法的高阶交互。我们证明，尽管RouteNet最初被表述为异构图神经网络，但它实际上是一种新型TDL的首个实例化实现。具体而言，本文提出了OrdGCCN这一新颖的TDL框架，该框架引入了任意离散拓扑空间中有序邻域的概念，并证明RouteNet的架构可被自然地描述为有序拓扑神经网络。据我们所知，这标志着前沿TDL原理在现实世界中的首次成功应用——我们通过大量测试床实验验证了这一结论，为下一代基于有序TDL的应用奠定了理论基础。