# Graph Neural Network based Service Function Chaining for Automatic Network Control

arXiv: https://arxiv.org/abs/2009.05240 · HTML: https://mlsanity.com/paper/2009.05240/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains](https://mlsanity.com/paper/2109.14276/) (2021)
- [Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning](https://mlsanity.com/paper/2504.18902/) (2025)
- [Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management](https://mlsanity.com/paper/2506.13153/) (2025)
- [A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case](https://mlsanity.com/paper/2212.00714/) (2022)
- [Advanced Scaling Methods for VNF deployment with Reinforcement Learning](https://mlsanity.com/paper/2301.08325/) (2023)

## Abstract (arXiv)

Software-defined networking (SDN) and the network function virtualization (NFV) led to great developments in software based control technology by decreasing expenditures. Service function chaining (SFC) is an important technology to find efficient paths in network servers to process all of the requested virtualized network functions (VNF). However, SFC is challenging since it has to maintain high Quality of Service (QoS) even for complicated situations. Although some works have been conducted for such tasks with high-level intelligent models like deep neural networks (DNNs), those approaches are not efficient in utilizing the topology information of networks and cannot be applied to networks with dynamically changing topology since their models assume that the topology is fixed. In this paper, we propose a new neural network architecture for SFC, which is based on graph neural network (GNN) considering the graph-structured properties of network topology. The proposed SFC model consists of an encoder and a decoder, where the encoder finds the representation of the network topology, and then the decoder estimates probabilities of neighborhood nodes and their probabilities to process a VNF. In the experiments, our proposed architecture outperformed previous performances of DNN based baseline model. Moreover, the GNN based model can be applied to a new network topology without re-designing and re-training.