## 5 Experimental Results and Analysis  
### Experiment Settings  
**Dataset:** Figure 2 shows the statistics of 34 users' historical records in a month. Among these, user D05 has the least with only 45 records, whereas user D03 has the most with a total of 2718 records. It can be seen that the number of users' historical records within a month can be divided into five stages from 0 to 2500. In order to make the experimental results more credible and make the user data more random, users in each stage are selected separately, and finally the data of ten users, i.e., A01, B07, A11, D00, A03, A12, B06, B08, A04, and A07, are selected for the experiment.  
**Data pre-processing:** Based on the idea of horizontal federated learning, the model used by each terminal is the same, and the model parameter settings are also the same. In the representation learning model for the attributed heterogeneous network, the parameter dimension setting is related to the feature vector, and the feature information about the location node is related to the number of connected base stations in the historical records. However, in horizontal federated learning, each terminal needs to get the same feature, so the dimension of the feature vector needs to be fixed. The App node features are obtained according to the App node types. The frequency of each type of application is counted, a fixed number of high-frequency types are selected, and the remaining types are classified into one category. In the same manner, the location node features are obtained according to the coverage area of the base station, the usage frequency of each coverage area is counted, a fixed number of high-frequency base stations LAC are selected, and the remaining types are classified into one category. When a user's App types or the number of coverage areas is less than a fixed number, 0 is used to fill the one-hot encoding value to obtain the feature vector of the same dimension.  
**Basic settings:** In this section, we conduct an experiment and evaluation on the proposed FL-AHNEAP method still based on the LiveLab App usage dataset, using the prediction accuracy as the evaluation criterion. During terminal training, we use 80% of the selected training dataset as the training set and 20% as the test set.  
In this experiment, ten users--A01, B07, A11, D00, A03, A12, B06, B08, A04, and A07--were selected as the representative users to realize the mobile App start-up prediction method based on federated learning. Because it is a simulation experiment, and there are restrictions on the number of users in the dataset, we did not select many users to participate in the training, and did not randomly selected users for the training. In each round of communication, the clients corresponding to these 10 users were directly selected to perform the aggregation of the shared model parameters, which is equivalent to the parallelization of synchronous data in distributed learning, and all user data were selected to train the shared model. Then each terminal trained a personalized link prediction model based on the neural network according to the shared model. During prediction, each terminal first generates the node embeddings of the current time, location, and previous App according to the shared model, and then aggregates the node embeddings as the input, obtains the prediction probability based on the neural network model, and generates the candidate App set.  
Figure 2: LiveLab dataset: statistics of 34 users’ historical records in a month.