# A Graph Convolutional Network-Based Method for Congested Link Identification
## 4 Performance Evaluation
### Performance Evaluation Results  
We simulate network congestion by assigning a congestion probability to each of the \(n\) links. The congestion was caused by limited link performance or insufficient resources from the link's originator node. In this paper, we introduce the concept of congestion caused by node factors, which means that when a node is congested, all the links originating from that node are congested. Each link \(l_{k}\) in the network is congested with a probability \(p_{l}^{k}\) distributed uniformly in \([0,\delta_{l}]\). Each node \(n_{k}\) becomes congested with a probability \(p_{n}^{k}\) uniformly distributed in \([0,\delta_{n}]\), where \(\delta_{l}\) is the maximum link congestion probability threshold and \(\delta_{n}\) is the maximum congestion caused by the node factor probability threshold. The values of \(\delta_{l}\) and \(\delta_{n}\) were varied in our simulations to evaluate the algorithm under different situations. 1. Performance under different numbers of hidden units  
The number of hidden units is an essential parameter in the GCN-based algorithm, since different numbers of hidden units can significantly influence the performance of the GCN model [35]. We chose the network topology shown in Figure 3 and measured the performance of our GIC algorithm with the values of the DR and FPR for different numbers of hidden units. We set \(\delta_{n}=0.09\) and \(\delta_{l}=0.09\). As shown in Figure 10, the \(x\) axis represents the number of hidden units. It can be seen from Figure 10 that when the number of hidden units increased, the performance of the model rose at first and then decreased. The main reason for this is that when the number of hidden units exceeds a certain degree, the model complexity and the computation difficulty increase, which leads to overfitting [35]. 2. Performance versus different channel cause congestion probabilities  
We compared our algorithm with SCFS [12], CLINK [13], and RIC [26]. To test the impact of the channel cause congestion probability on the performance of the algorithms,  
Figure 10: Performance under different hidden units. given that most of the aforementioned algorithms neglect the impact of node congestion, in order to compare the performance of the algorithm proposed in this paper under the condition where node congestion was not considered, we set \(\delta_{n}=0\) and then switched \(\delta_{l}\) from 0 to 0.3. The results are shown in Figures 11 and 12. As the channel cause congestion probability increased, the performance of all congested link identification algorithms degraded because as the number of congested links increased, the number of congested routes also increased. A large number of congested path routes would increase the number of feasible solutions, which increased the probability of errors in the identification algorithm. More importantly, as the network congestion level increased, the chance of multiple congested links in the same path increased, but the existing algorithms are all based on the presence of only one congested link, which reduced the DR of the algorithms. However, the performance of the FPR was not all that different, since the existing algorithms are conservative in the inference of congestion. Figure 11: DR versus different channel cause congestion probabilities. Figure 12: FPR versus different channel cause congestion probabilities. topology. Under different congestion types caused by node factor probabilities, both the DR and FPR of the proposed congested link identification algorithm based on the GCN had optimal performance, and the DR was far superior to that of the previous algorithm. (4) Performances of various topology sizes  
To validate the applicability of our algorithm, we conducted experiments on a series of binary tree-based topologies, which are illustrated in Figure 15. These topologies offer a contrasting structure to our initial evaluation scenario, enabling us to thoroughly assess the robustness of the diagnosis methods.