## 4 Results
### Influence Simulation  
In the following experiments, the bots acted as influencers and had to strike a balance between avoiding detection and expanding their influence. Similar experiments were conducted to assess the performance of different variations of the bot strategy model. The only difference is that the fake account reward function was replaced by the influencer reward function, introducing \(\theta\). The bot strategy models are very sensitive to \(\theta\), which controls the balance. To prevent the bots from focusing on just one goal, \(\theta\) could not be fixed. Instead, \(\theta\) was adjusted to different values to suit different models, as shown in Table 2.  
The ability of the bots to avoid detection reduced overall. When using the RF detector, the survival rate of the baseline model, additional actions model, and the combined model decreased by 21.3%, 22.3, and 14.8%. Other variations of bot strategy models did not suffer significant decreases in survival rate, but their average bot scores increased by around 0.06. More decreases in survival rate were observed with BotRGCN. The survival rates of the baseline model, the additional actions model, the additional actions \(+\) profile editor model, and the combined model decreased by 24.4%, 67.1%, 22.8%, and 14.9%, respectively. However, the additional actions \(+\) preselection model achieved a remarkably strong performance. Not only did it increase the survival rate by 12.2% and decrease the average bot score by 0.026, but it also achieved the best performance in propagating influence. Expanding the influence by interacting with the preselected users and spamming posts was surprisingly beneficial for concealing the bots in this situation. It is important to note that our previous findings indicate that introducing preselection would significantly reduce the survival rate when the bot operates as a fake account. This highlights the interconnection of different bot characteristics, which collectively affect the performance of detection systems, making it more difficult for bot developers to refine their strategies. From the detectors' perspective, this poses a risk as well; the additional actions \(+\) preselection model exploited the vulnerability of BotRGCN. The impact of bots is concerning. The experiment herein demonstrates that if the RF detector is implemented, the additional actions \(+\) profile editor model will attract 36,320 followers, resulting in a total of 7,878,764 views over 1000 simulations. Although BotRGCN was able to identify and suspend many bots before the simulation concluded, a significant impact remained. With the additional actions \(+\) preselection model, this approach attracted 39,542 followers and generated 8,932,088 views, which is even higher than the follower and view counts achieved when using the RF detector. The number of followers and views throughout the experiments are shown in Figure 6. The RF detector still failed to detect bots after the first detection, leading to steady growth in the number of followers and an increasing growth rate in the number of views. On the other hand, BotRGCN slowed the growth of followers and views overall by reducing the number of