Scaling Graph Neural Networks (GNN) for Real-Time Modeling of Network Behaviour
- Publisher:
- Association for Computing Machinery (ACM)
- Publication Type:
- Conference Proceeding
- Citation:
- SIGCOMM 2025 Proceedings of the 2025 ACM SIGCOMM 2025 Posters and Demos, 2025, pp. 46-48
- Issue Date:
- 2025-09-10
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Network modeling has long been a well-established field of study. More recently, Graph Neural Network based models have demonstrated remarkable capability in capturing complex interactions in network data without assumptions about physical networks. While this characteristic facilitates integration across various telecom access networks, current benchmark models remain impractical for real-world deployment, due to real-time demands of modern infrastructure. This research develops a scalable solution for network modeling in large-scale domains such as telecommunication networks. By incorporating distributed learning into the architecture, we propose a novel framework that addresses computational inefficiency without compromising the accuracy offered by benchmark GNN-based models. The proposed architecture supports deeper and larger graphs, and natively handles fragmented datasets, reducing reliance on centralized aggregation and improving compatibility with real-world infrastructure. Beyond scalability, the design emphasizes stable optimization and resilience to enhance reliability in production environments. When applied to the state-of-the-art model, our proposed architecture outperforms the original, achieving a Pearson correlation of 0.999 with MSE under 0.0005. It also converges faster, with inference speedup scaling proportionally to the number of nodes. In a single-node, two-worker setup, it achieves ∼48% inference speedup, with overall training efficiency improving by 20%, highlighting practical benefits for real-world scenarios.
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