## 3 Methodology
### Attributed KNN Graph  
In the existing research in the field of cellular traffic prediction, the fusion of multi-modal features often uses multiple undirected graphs to input into the neural network. Although this method can provide more abundant information for the model, it also improves the complexity and running time of the model. In addition, the method of expressing the correlation of each region by a 0.1 matrix cannot accurately extract the complex spatial and temporal distribution of cellular traffic, which is due to the large difference in the traffic distribution pattern of adjacent regions. Therefore, this paper selected the most similar K nodes for prediction by constructing the attributed KNN graph, and the multi-modal features were directly spliced after the historical time series. This approach not only reduced the complexity of the model, but also improved the prediction accuracy of the model. The multi-modal features encompass comprehensive information pertaining to cellular traffic, encompassing the time of traffic occurrence, the volume of traffic at that specific time, the quantity of base stations (BSs), the quantity of points of interest (POIs), and social activities (SAs). The number of base stations reflects the peak value of cellular traffic in this region. No matter how large the traffic load demand in this region is, the peak value of traffic will not exceed the maximum bearing range of the base station. Points of interest (POI) within the region offer insights into the extent of population concentration, with areas containing a larger number of POIs indicating a higher degree of aggregation, as well as SAs. The correlation between multi-modal features and the distribution of cellular traffic, as well as the spatial distribution characteristics of cellular traffic, are depicted in Figure 1.  
Consequently, we established an attributed KNN graph by evaluating the similarity between cellular traffic in different regions. The specific procedure for transforming cellular traffic into a KNN graph with node attributes can be observed in Figure 2.  
Figure 1: (**a**) The correlation between multi-modal features and the distribution of cellular traffic. (**b**) Spatial distribution of cellular network traffic (the data in (**b**) is from the dataset [29] used in this paper, which divides the Italian city of Milan into 10,000 regions to form a 100 \(\times\) 100 square matrix). The attributed KNN graph can be comprehended as a type of similarity matrix. The measurement of the distance between regions is based on the spatial attributes of the cellular traffic. Regions that are closer exhibit a higher similarity, whereas regions that are farther apart display lower levels of similarity, and in some cases, may even be considered insignificant. However, not all spatial distributions are related to distance. Some regions are very close to each other, but the traffic distribution patterns are completely different; some regions are far apart, but the traffic distribution patterns are very similar. Therefore, this paper first selected a target node and obtained the similarity matrix \(X\) between the node and the other nodes by heat kernel [30] calculations. According to the similarity, \(K\) nodes with the closest attributes were selected from high to low. Each node represents the cellular traffic data in a region (for example, the Milan dataset used in this paper divided Milan into a 100 \(\times\) 100 matrix; so, the number of nodes was 10,000). The cellular traffic data of these \(K\) nodes was extracted and rearranged into a \(L\times K\) matrix, namely KNN spatio-temporal matrix \(B\), where \(K\) is the number of the most similar \(K\) nodes and \(L\) is the length of the time series. Then, the number of BS, POIs, and SAs in the region corresponding to these \(K\) nodes was extracted to form three \(1\times K\) matrices, namely matrix \(Mb\), matrix \(Mp\), and matrix \(Ms\).