# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 4 Experimental Evaluation
### Performance Analysis  
**Performance of GE. ** Figure 3 shows the comparison of prediction accuracy for the GATNE-I model alone, where we use the _interval of day_ scale as time node. We regarded the maximum accuracy during all epochs as the result. The evaluation calculates _Accuracy@4_ respectively in three types of sub-graphs. The figure shows accuracy when predicting four candidate Apps with five users. According to the above experiments of five users, we can preliminarily infer that the influence of various factors on the prediction is related to the user's usage habits, and each user is more or less different from others. Take B02 as an example first. As Figure 3 shows, obviously time and the latest App have more impact on prediction than location. The accuracy of B02 is finally stabilized at about 70% with \"usedTime\" type, while about 60% with \"precede\" type. Although accuracy with \"usedLocation\" type is on the rise, the rate is just 33%, far from 70%. Furthermore, the magnitude of the upward tendency is slow. In general, using time information to predict Apps is better than location information or previous Apps with user B04 and B02; however, results of users A07, A04, and A12 are different. **Performance of our framework. ** We'll perform the _Mean_ method as contrast experiments, which is based on the GATNE-I model. The _Mean_ method firstly gets the similarity of each App node and  
Figure 3: The comparison of prediction _Accuracy@4_ for the GATNE-I model alone, GE: App and time, App and location, App and the previous App at _interval of day_ scale. time node, location node, and the latest App node, respectively, from three sub-graphs. Then, calculate the mean of three similarities with the same App as the final score of each application, and find the top-k application based on the similarity ranking. Figure 4 shows the _Accuracy@4_ of five users with the _Mean_ method and AHNEAP. As the figure shows, there is higher accuracy using AHNEAP than the _Mean_ method, which achieves the highest 80% for user B02 and improves by highest about 20% for user A07. Therefore, according to all our experiments of five users, we can preliminary infer that this conclusion is universal that AHNEAP outperforms the _Mean_. Seen from all the whole process of the _Mean_ method and our framework AHNEAP in Figure 5, there is only slight undulation about accuracy during all epochs in AHNEAP while the _mean_ is not. In other words, we can achieve a stable state through fewer epochs using AHNEAP than the mean method, further consuming fewer resources. Thus, both in terms of accuracy and resources, we still choose the neural network to fit our context. Otherwise, the _mean_ is difficult to choose the proportion of three types of parameters, but we might as well directly train the weight matrix. **Performance of comparison of interval and hour scale. ** Figure 6 shows that taking _interval of day_ as time nodes are almost better than _hour of day_ with five users except A12 when predicting top-4 candidate applications overall. However, it just achieves the highest about 2% for user A12 at _hour of day_ temporal scale than at _interval of day_ temporal scale. For users A07, A04, and B04, the accuracy is improved only by less than 1%. However, the accuracy increases by about 7% for user B02 at the scale of _interval of day_. According to the above experiments of five users, we indicate that the two temporal scales have roughly the same impact although there are little deviations for each user. In addition, fewer node pairs are needing to be trained when transforming interval information to time node  
Figure 4: The comparison of prediction _Accuracy@4_ for the _Mean_ and our framework AHNEAP with five users at _interval of day_ scale. Figure 5: Prediction accuracy of B02 in all epochs. because there are fewer time nodes generated in the training process. However, from the aspect of effectiveness for a given period, the _interval of day_ scale is not as effective as the _hour of day_.