## 2 Materials and Methods
### Methods  
Stage 1: Difference analysis. In order to reveal the relationship between social characteristics and achievement levels, we first compared the differences in social traits among groups with varying achievement levels at the numerical level.  
Stage 2: Correlation analysis. In this stage, Pearson Correlation was applied to quantify the correlation between academic performance and social characteristics. Social characteristics include both the quantity of peers and the quality of peers.  
For the quantity of peers indicator, we use the number of socializations directly. For the indicator of peer quality, we use a weighted average calculation. In this calculation, the academic performance of the peers is combined with the number of socializations as a weight and finally normalized.  
Stage 3: Predictive validity validation. Finally, to explore the feasibility of predicting grades from social data, we used four common machine learning classification algorithms: Random Forest (RF) [46], a Neural Network (DNN) [47], a Graph Convolutional Network (GCN) [48], and a Dynamic Graph Convolutional Network (DGCN) [49], to predict the academic performance categories of students.  
All four algorithms are applicable to multi-classification tasks. RF belongs to ensemble learning, where it obtains the classification of samples by voting after inputting sample feature data. The DNN learns sample features and performs classification through a multi-layer network structure. The GCN requires not only node features as input, but also the connections between nodes. The DGCN goes a step further by incorporating temporal information. These methods are based on different theoretical foundations and progressively use more information, which can help us to observe the impact of differences in information richness on the classification results.  
For the Random Forest and Neural Network models, we used the number of socializations of students per month in five categories, including totals, roommates, classmates, and opposite sex, along with an other category, totaling 60 feature values, with academic categories as labels for classification experiments.  
For graph networks, we considered students as nodes and the social relationships generated between them as edges, with the number of social relationships as degrees. If the number of co-occurrences of Student A and Student B was greater than the threshold, an edge was constructed to connect node A to node B. The number of times A and B co-occurred was the degree of this edge. The Dynamic Graph Network incorporated time information compared to the standard Graph Network; specifically, the network was split by month, resulting in a dynamic network that changed over time, as shown in Figure 4.  
Figure 3: Total number of consumption records and co-occurrences per month.  
We evaluated all methods using ten-fold cross-validation. To account for the random nature of the computation, we ran each experiment ten times and calculated the average as the final result for each method. The problem is multi-categorical and has a data imbalance, so we chose four metrics for model evaluation: accuracy, weighted average precision, weighted average recall, and weighted average F1 score. The weighted averages use the percentage of each category in the total number of samples as weights.