## 3 Graph Neural Networks for Next-Generation Networks
### Link Scheduling
#### 3.2.2 Traffic Prediction  
Traffic prediction aims to anticipate the volume of network traffic based on historical data. This proactive measure helps to prevent future congestion and allows for dynamic optimization of network resources [66]. Like with IDS, traffic prediction can greatly benefit from GNN-based models due to their ability to comprehend and model network data effectively. In other words, as the traffic at a particular node depends not only on its historical values but also the traffic conditions of its near or far neighboring nodes, GNN combined with some temporal models (e.g., Recurrent neural network (RNN) or Long short-term memory (LSTM) model) can well model these dependencies [67]. In this context, [PERSON] et al. [68] use GNN with RNN to extract spatiotemporal dependency from inter-tower and in-tower traffic and predict cellular traffic at a large scale. The proposed model outperforms the state-of-the-art approaches by 13.2% and 17.5% in terms of \(MAE\) and \(MAPE\), respectively. [PERSON] et al. [69] introduce the _Graph Attention Spatial-Temporal Network_ (GASTN) to forecast the mobile traffic. GASTN takes into account spatial dependencies by utilizing a geographical relation graph. Moreover, the model integrates RNN to extract temporal features within sequential data. Additionally, GASTN introduces two attention mechanisms to incorporate two distinct effects holistically. By incorporating the attention mechanism, GASTN can consider various factors and their importance when predicting traffic. In another work, [PERSON] et al. [70] present _STEM-GCN_, a new GCN exploiting semi-variogram, which is a measure of dissimilarity between two variables across the domain of spatial distance and time lags. This model effectively captures spatial and temporal dependencies from dynamic graphs, includes a correlation smoothing strategy to reduce noise and improve link prediction accuracy, and handles network dynamics by propagating spatial and temporal characteristics by using stacked memory cell structures across sequential time steps. [PERSON] et al. [71] propose the _Spatio-Temporal Hybrid Graph Convolutional Network model_ (STHGCN), which uses Gated recurrent units (GRUs) to model temporal dependencies and a hybrid GCN to capture complex spatial dependencies from three perspectives: (i) spatial proximity (SP), (ii) functional similarity (FS), and (iii) recent trend similarity (RTS). As shown in Figure 4, the model processes temporal slices of features from each BS and applies the hybrid graph CNN using the three aforementioned spatial relationships. The output is processed by a GRU along with external information such as weather data or metadata and is then handled by a fully connected network to generate the final traffic prediction. While the aforementioned approaches have demonstrated efficiency in traffic prediction, they do not take into account the similarity among different types of cellular services (e.g., calls, internet) and regions. To address this issue, [PERSON] et al. [72] introduce a transfer learning strategy based on a GCN model for large-scale traffic prediction called _STA-GCN_. The proposed model integrates transfer learning, an attention mechanism, the CNN model, and GCN to capture both temporal and spatial dependencies. The experiments conducted with the Telecom Italia dataset validated the efficacy of transfer learning in enhancing knowledge reusability and accelerating model fitting, without com  
Figure 4: The architecture of STHGCN [71]. promising prediction accuracy. Moreover, traffic in a wireless network is impacted not only by historical traffic and cross-domain datasets but also by handover traffic from the BS. Consequently, [PERSON] et al. [73] propose the _STGCN-HO_ model, which uses a handover graph for cellular traffic prediction. The model leverages the transition probability matrix of the handover graph to enhance network traffic forecasting and fuses features from auxiliary data (e.g., day of the week, hour of the day), spatial, and temporal domains.