# Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities

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## Abstract

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## 1. Introduction

#### 1.1. Existing Reviews

#### 1.2. Our Contributions

- A review of DTs applications and use cases for future networks, including both access and core networks.
- A review of several GNN-based models for network management, classified by problems and network domains.
- An analysis of potential applications of DTs based on GNNs for the Next-generation networks (GraphNDTs) and a study of existing works in this direction.
- Insights into challenges and future directions to be taken in order to improve the GraphNDTs to handle large-scale networks and their inherent sub-challenges.

#### 1.3. Structure of the Survey

## 2. Digital Twins for Next-Generation Networks

#### 2.1. Digital Twins for Access Networks

#### 2.1.1. Radio Networks

#### 2.1.2. Internet of Things Networks

#### 2.1.3. Vehicular Networks

#### 2.1.4. Edge Networks

#### 2.2. Digital Twins for Core Networks

#### Lessons Learned

## 3. Graph Neural Networks for Next-Generation Networks

#### 3.1. Backgrounds on Graph Neural Networks

#### 3.1.1. Spectral-Based Graph Convolutional Networks

#### 3.1.2. Message-Passing-Based Graph Neural Networks

#### 3.2. GNN in Access Networks

#### 3.2.1. Resource Allocation

Ref. | Model | Baseline | Objective | Metric |
---|---|---|---|---|

[57] | DRL, GNN | Default | Throughput and coverage maximization and load balancing | Gain percentages |

[58] | DRL, GCN | Random, CNN+DRL, PG | Data rate maximization | Data rate and convergence performances |

[59] | DQN, GAT, A2C | DQN, A2C, Hard slicing | Data rate maximization while guaranteeing Quality of service (QoS) | Utility performances |

[60] | GCN, Spectral clustering | Max. achievable and max. sum of rate and power | Power allocation and user association | Data rate |

[61] | GCN, DNN | Default | Sum rate maximization | Accuracy, average sum rate |

[62] | GCN, MWIS | Local greedy solver | Delay minimization | Accuracy, average sum rate |

#### Connection Management

#### Link Scheduling

#### 3.2.2. Traffic Prediction

#### 3.2.3. Intrusive Detection

Ref. | Learning | Target | Models | Dataset | Performance |
---|---|---|---|---|---|

[81] | Supervised | Node | GCN | - | Acc: 99.51%, 99.03% |

[82] | Supervised | Node | Inferential SIR-GN | CAIDA + synthetic data | F1: 97.85–99.78% |

[83] | Supervised | Node | GNN, GRU | CIC-IDS2017 | F1: 99% |

[84] | Supervised | Node | Graph network | CTU-13 + synthetic data | ACC: 96% |

[85] | Supervised | Edge | E-GraphSAGE, GraphSAGE | BoT-IoT, ToN-IoT, NF-BoT-IoT, NF-ToN-IoT | F1: 100%, 99%, 97%, 100% |

[86] | Supervised | Node | E-GraphSAGE, GAT | UNSW-NB15, CIC-Darknet2020, ToN-IoT, CSE-CIC-IDS2018 | F1: 99.5%, 92.32%, 99.88%, 96.5% |

[87] | Supervised | Node | GDN, GAT | SWaT, WADI | Acc: 99%, 98% |

[88] | Semi-supervised | Node | GCN | CTU-13, Honeypot dataset | F1: 98.27%, 98.22% |

[89] | Semi-supervised | Edge | GNN, Autoencoder, Attention mechanism | LANL2015, CERT, PicoDomain | F1: 89.28%, 91.28%, 92.68% |

[90] | Supervised | Node | GIN, GNNExplainer | - | F1: 99.52%, 99.47% |

[91] | Supervised | Node | DB-GNNExplainer | - | F1: 99.14% |

#### 3.3. GNN in Core Networks

#### 3.3.1. Resource Allocation

#### 3.3.2. Routing

#### Lesson Learned

## 4. GNN-Based Digital Twins for Next-Generation Networks

#### 4.1. Major Benefits of Using GNN-Based Digital Twins in Next-Generation Networks

#### 4.1.1. Network Optimization

#### 4.1.2. Low-Cost Trials

#### 4.1.3. Predictive Maintenance

#### 4.2. Existing Studies

#### 4.2.1. Routing Optimization

#### 4.2.2. Network Slicing Management

## 5. Challenges and Future Directions

#### 5.1. Dynamicity

#### 5.2. Heterogeneity

#### 5.3. Robustness

#### 5.4. Generalization

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

CSI | Channel state information |

LSTM | Long short-term memory |

GNN | Graph neural network |

GCN | Graph convolutional network |

CNN | Convolutional neural network |

RNN | Recurrent neural network |

GSP | Graph signal processing |

GAT | Graph attention network |

MAPE | Mean average percentage error |

GRU | Gated recurrent unit |

FL | Federated learning |

URLLC | Ultra-reliable low-latency communication |

UE | User equipment |

SLA | Service level agreement |

E2E | End-to-end |

NDT | Network digital twin |

DT | Digital twin |

DRL | Deep reinforcement learning |

RL | Reinforcement learning |

MARL | Multi-agent reinforcement learning |

BS | Base station |

RAN | Radio access network |

VNE | Virtual network embedding |

SFC | Service function chain |

VNF | Virtual network function |

NFV | Network function virtualization |

DL | Deep learning |

SDN | Software-defined network |

MPNN | Message-passing neural network |

MEC | Multiple-access edge computing |

IoT | Internet of things |

IIoT | Industrial internet of things |

ML | Machine learning |

DDoS | Distributed denial of service |

YANG | Yet another next generation |

QoS | Quality of service |

GFT | Graph fourier transformation |

GraphNDT | Graph neural network-based network digital twin |

AI | Artificial intelligence |

NGN | Next-generation network |

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**Figure 2.**A reference architecture of a Network digital twin (NDT) [25].

**Figure 3.**A simplified illustration of a Message-passing neural network (MPNN) on an undirected graph where we aggregate neighbor features by “mixing” them and updating the target node (the red one) using concatenation.

**Figure 4.**The architecture of STHGCN [71].

**Figure 5.**(

**a**) Scheme of Conventional machine learning-based NIDS using network flow. (

**b**) Graph-based representation of some known attacks, ${a}_{n}$ nodes refer to the attackers, ${v}_{n}$ nodes represent the targets, and ${f}_{n}$ nodes represent the different flows.

**Figure 6.**A calculation example of GNN [96].

**Figure 7.**Illustrations of three major use cases of a Graph neural network-based network digital twin (GraphNDT) including (from left to right): network optimization (yellow), low-cost trials (green), and predictive maintenance (blue).

**Table 1.**Summary of related reviews on DT and GNN related topic (✓, x, and * indicate that the topic is totally, not, or partially covered, respectively).

Ref. | Contributions | GNN | DT | Networking Applications |
---|---|---|---|---|

[5] | A comprehensive review of various combinatorial optimization problems on graphs, with a particular emphasis on their applications in the telecommunications domain. | * | x | * |

[7] | A comprehensive overview of the application of GNN in wireless networks. | ✓ | x | * |

[4,8] | A comprehensive review of graph-based deep learning models for solving problems in different types of communication networks including wireless, wired, and software-defined networks. | ✓ | x | ✓ |

[12] | A comprehensive review of recent advances in the application of GNN to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments. | ✓ | x | * |

[13] | A survey on traffic forecasting in the intelligent transportation system. | ✓ | x | x |

[14] | A brief review on the application of DT with 5G networks and beyond. | x | ✓ | ✓ |

[15] | A comprehensive survey on the digital twin network including the key features, technical challenges, and potential applications. | x | ✓ | * |

[3] | A review of the potential use cases and scenarios in a 6G communication network where a digital twin could play an essential role. | x | * | ✓ |

[16] | A comprehensive survey on the benefits of twins for wireless and wireless for twins. | x | ✓ | ✓ |

[1] | Presentation of the application of GNN for the core components of network digital twins and coupling a network optimizer with the network digital twins. | * | ✓ | ✓ |

Ours | A review of DT, GNN, and Graph neural network-based network digital twin (GraphNDT) in innovating the communication networks. | ✓ | ✓ | ✓ |

Ref. | Models | Dataset | Baselines | Prediction Horizon | Performance |
---|---|---|---|---|---|

[68] | GNN | Unpublished | ARIMA, HW, LSTM | 30 min | MARE: 0.79 |

[69] | GNN, RNN, Attention mechanism | Telecom Italia | HA, ARIMA, MLP, LSTM, CNN-LSTM | 10 min | MAE: 30.93 |

[71] | GCN, GRU | Unpublished | HA, GCN, Attention GCN, DCRNN, Graph WaveNet | 15 min | MAE: 21.7, RMSE: 47.2 |

[72] | Transfer learning, Attention mechanism, CNNs, and GCN | Telecom Italia | LSRM, STGCN, ASTGCN, DCRNN | 1 h | MAE: 55.46, RMSE: 116.92 |

[73] | GCN, Gated Linear Unit | Unpublished | HW, ARIMA, LSTM | 15 min | RMSE: $3.91\times {10}^{5}$ |

[77] | GCN, GRU | Unpublished | ARIMA, LSTM, GNN | 5 s | MARE: $2.96\times {10}^{4}$ |

[78] | GNN, GAT | Telecom Italia | GNN, GRU | 10 min | MAE: 25.75, MAPE: 0.13, RMSE: 35.94 |

Ref. | Model | Baseline | Objective | Metric |
---|---|---|---|---|

[94] | GCN, DRL | R-ViNE, D-ViNE, GRC, MCVNE, and NodeRank | VNE | Acceptance ratio, average revenue latency, node, and link resource utility |

[95] | GNN, DRL | DDQN, MSGAS, Eigendecomposition | VNF placement | SFC rejection ratio, computation time |

[96] | GNN, DRL | OFM, size-greedy, pairwise, random | Reduce flow migration cost | Migration cost, computation time |

[97] | GNN, Kmeans | FirstFit, BestFit, GRC, NeuroViNE | Improve runtime and performance | Parallelizability, acceptance ratio, revenue and cost, CPU and link utilization |

[98] | GNN, RL | NodeRank, MCST-VNE, GCN-VNE | Dynamic VNE, reduce resource fragmentation | Revenue, acceptance rate |

[99] | GNN, ILP | DNN-based model | SFC | Cost (delay): average, fail ratio, overmax |

[100] | GCN, DRL | LDG, DNN-DDQN | SFC | E2E delay |

Ref. | Model | Baseline | Simulator | Metric |
---|---|---|---|---|

[105,106,107,108,109,110] | MPNN | Queueing theory, Fluid model, RouteNet | OMNeT++ | Delays in queuing networks |

[111] | MPNN | Genetic Algorithm | - | Edge utilization |

[112] | MPNN, DRL | Equal-cost multi-path, DRL [113,114] | OMNeT++ | Average E2E delay |

[115] | MPNN, DRL | Shortest path, Q-Routing [116] | Mininet, Ryu | Packet delivery ratio and transmission delay |

[117] | GCN, DRL | OSPF, DRL [118] | OMNeT++ | Packet loss rate, average delay, total number of packets forwarded |

Challenges | Description | Future Directions |
---|---|---|

Dynamicity | The network topology is dynamic in a way that nodes or edges may appear or disappear, and the input data change over time. | 1. Incremental Learning can allow GNN-based models learning from new data without having to retrain from scratch, which could be particularly beneficial in dynamic environments where data continually evolve [123]. 2. FL plays a crucial role within the dynamic GNN model, particularly in handling complex, large scales, and adapting to shifts in the network structure over time [124,125]. |

Heterogeneity | The different types of nodes and edges have different attributes, which are usually located in different feature spaces. | 1. Meta-path Aggregated Graph Neural Network (MAGNN) which can capture both the semantic and structural information in heterogeneous graphs. 2. Contrastive learning-based method, where the model is pre-trained in a self-supervised manner to learn both the semantic and structural properties of heterogeneous graphs. |

Robustness | The ability of the model to maintain high performance even when faced with perturbations in the graph structure or feature information. | 1. Robust GNN architectures that are inherently more robust against adversarial attacks [126,127]. 2. Adversarial training methods involve integrating adversarial examples into the training process [128]. |

Generalization | The ability to generalize GNN so that the GraphNDT can still provide accurate predictions in case of abnormal scenarios or configurations. This is mandatory for low-cost trial use cases. | 1. Train the models on additional data generated by simulations and testbeds. Transfer learning can be adopted to finetune the pre-trained models on the current NDT. 2. FL enables collaborative learning and model aggregation, allowing the GraphNDT to benefit from a broader understanding of various configurations while preserving the privacy of individual network systems [129,130,131]. |

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**MDPI and ACS Style**

Ngo, D.-T.; Aouedi, O.; Piamrat, K.; Hassan, T.; Raipin-Parvédy, P.
Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities. *Future Internet* **2023**, *15*, 377.
https://doi.org/10.3390/fi15120377

**AMA Style**

Ngo D-T, Aouedi O, Piamrat K, Hassan T, Raipin-Parvédy P.
Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities. *Future Internet*. 2023; 15(12):377.
https://doi.org/10.3390/fi15120377

**Chicago/Turabian Style**

Ngo, Duc-Thinh, Ons Aouedi, Kandaraj Piamrat, Thomas Hassan, and Philippe Raipin-Parvédy.
2023. "Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities" *Future Internet* 15, no. 12: 377.
https://doi.org/10.3390/fi15120377