Ordered Topological Deep Learning: a Network Modeling Case Study

2025-03-20

AI Summary

Traditional network performance prediction methods—such as packet-level simulation and queuing theory—suffer from high computational overhead, overly restrictive assumptions, and a fundamental trade-off between accuracy and efficiency. To address this, this paper identifies RouteNet’s underlying principle as *ordered topological deep learning* (TDL), a novel paradigm that explicitly encodes sequential neighbor relationships in network topologies. We propose OrdGCCN, an innovative heterogeneous graph neural network framework grounded in ordered TDL, enabling end-to-end differentiable network emulation. Unlike prior approaches, OrdGCCN natively models the inherent ordering of topological dependencies—e.g., path sequences and hierarchical forwarding structures—without relying on hand-crafted abstractions. Evaluated on real-world testbeds, OrdGCCN achieves substantial improvements in prediction accuracy for critical metrics—including latency and throughput—while maintaining low inference latency. This work pioneers the practical application of ordered TDL to communication infrastructure modeling, establishing its feasibility and state-of-the-art efficacy.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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.

Problem

• Optimizing network performance with advanced modeling techniques
• Overcoming limitations of traditional network modeling methods
• Introducing ordered topological deep learning for network modeling

Innovation

• Introduces OrdGCCN for ordered topological learning
• Reformulates RouteNet as ordered TDL
• First real-world TDL application success