## 2 Materials and Methods  
### Datasets  
Our data came from a university in southern China. To ensure the security of the data, the university replaced the identity ID in the data with random UIDs, hiding the identity information of the students. The study was approved by the Key Laboratory of Neuroeconomics, Guangzhou Huashang College (GZHSC-202312001). This study focused on the weighted grade point average of the final grades of first-year undergraduate courses. The weighted GPA is very important for students and serves as a critical for various aspects such as scholarships, graduate school retention, graduation, and employment. It is calculated as follows:  
\[G=\frac{\sum_{i=1}^{n}(w_{i}\ *g_{i})}{\sum_{i=1}^{n}w_{i}}. \tag{1}\]  
where n is the total number of subjects, g is the grade for a single subject, and w is the number of credits for that subject. The academic performance data of the undergraduate students were obtained from the university's Academic Management System (AMS). Students were classified into different subgroups based on their weighted average grades: G \(<\) 60; 60 \(\leq\) G \(<\) 70; 70 \(\leq\) G \(<\) 80; 80 \(\leq\) G \(<\) 90; 90 \(\leq\) G \(<\) 100. These multiple subgroups help us understand and identify the behavioral characteristics of students in different achievement bands in a more nuanced way, while also allowing us to study academic difficulties. The social data came from the university's campus card system. This system records students' spending behaviors related to their campus cards, such as recharging, dining hall purchases, supermarket transactions, taking the bus, visiting the doctor, and so on. The dataset includes consumption time, consumption location, service window, spending amount, and card balance. The consumption time is generated in timestamp format; the consumption location indicates where the behavior occurs and is recorded using a uniform location identification system for the entire campus; the service window indicates the specific window where the consumption occurs; and the consumption amount indicates the size of the transaction. Table 1 shows the raw data segments. \begin{table}
\begin{tabular}{c c c c c c} \hline
**UID** & **Location** & **Time** & **Service Window** & **Spending** & **Balance** \\ \hline a & Canteen I & 2019/09/10 & 7 & 15 & 96 \\  & & 12:16:03 & 7 & 12 & 156.14 \\ b & Canteen I & 2019/09/10 & 7 & 12 & 156.14 \\ c & Hospital & 2019/09/11 & 1 & 3 & 165 \\ d & \(\cdots\) & \(\cdots\) & & & \\ e & Supermarket I & 2019/09/20 & 1 & 6 & 43 \\ f & Supermarket II & 16:26:54 & 2019/09/24 & 1 & 9.9 & 96.14 \\  & & 11:22:32 & 1 & & \\ \hline \end{tabular} All data are anonymized using UIDs.  
\end{table} 
Table 1: The raw data segments. Inspired by the concept of co-occurrence in the Apriori algorithm [44], we computed the co-occurring relationships of students in consumption records. Apriori is one of the classical methods for computing frequent itemsets. Frequent itemset mining is the basis for important data mining tasks such as association rules, correlation analysis, etc., and its essence is in calculating the number of co-occurrences of two objects. Similarly, in the consumption record data, if A and B appear in the same location at the same time, they are considered to co-occur, indicating that a social relationship exists, as shown in the first two records in Table 1. To rule out chance occurrences, we needed to set a threshold for the number of co-occurrences, i.e., to determine how many times the co-occurrence relationship should appear within one month for it to be considered valid. In some recent studies, [PERSON] et al. [35] explored the changes in peer network characteristics under different thresholds, and the changes in the association between academic performance, but they did not give the best choice of thresholds or a method for determining them.