## 2 AI Techniques in Communication Networks
### Graph Neural Networks (GNNs) for Network Structure Analysis  
GNNs offer significant benefits for network analysis by modeling networks as graphs, where nodes represent network components (e.g., routers, switches, or end devices) and edges represent the connections between them (e.g., communication links or data flows) [85]. GNNs have shown a 15% improvement in network throughput and a 10% reduction in latency compared to traditional methods of network analysis [86]. By learning the dependencies and interactions between different network components, GNNs are capable of optimizing network traffic, enhancing scalability, and improving resilience to failures [87]. One of the key strengths of GNNs is their ability to capture the relationships and dependencies between different elements of a network, allowing for a more holistic understanding of network behavior [88]. In practice, GNNs are used to identify the most efficient paths for data transmission, predict network congestion, and optimize routing decisions [89]. These improvements are especially important in high-demand communication networks, where maintaining low latency and high throughput is critical. To further explore the advantages of GNNs, I compare them with other AI models commonly used in network analysis. For instance, CNNs and RNNs are also utilized for tasks such as traffic prediction and anomaly detection. While CNNs are highly effective for spatial data analysis, their ability to model the complex dependencies between network components is limited compared to GNNs, which are explicitly designed to handle graph-structured data. On the other hand, RNNs are well suited for time-series data and sequential decision-making but may struggle with capturing the structural relationships between network components that GNNs excel at. As shown in Figure 3, GNNs outperform CNNs and RNNs in terms of both throughput improvement and latency reduction, particularly in scenarios where the underlying \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **NLP Model** & **Accuracy (\%)** & **Cost Reduction (\%)** \\ \hline Intrusion Detection & Transformer & 98 & N/A \\ Customer Service & BERT-based Chatbot & 90 & up to 50 \\ Threat Analysis & RNN & 85 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NLP applications in network security and customer service. network structure is crucial for optimizing performance. This makes GNNs particularly advantageous for large-scale, dynamic networks. To provide a comprehensive understanding of the contributions of various AI techniques to communication networks, Table 3 presents a benchmark comparison across the different applications discussed in this section. As shown in the table, each AI technique provides distinct advantages in addressing network challenges such as security, performance, and automation. NLP models, particularly in intrusion detection, demonstrate exceptional accuracy, reaching up to 98% for detecting anomalies in network logs, as well as significantly enhancing customer service automation. Meanwhile, graph neural networks (GNNs) excel in modeling network topologies, improving throughput by 15% and reducing latency by 10%. The versatility of these AI models in network applications highlights their importance in building more resilient, efficient, and adaptive communication infrastructures. In conclusion, the integration of AI techniques such as NLP and GNNs into communication networks not only improves the security and efficiency of operations but also fosters innovation in customer service automation and network performance. The comparative performance data underscore the value of each approach, allowing network administ  
Figure 3: Performance comparison of GNN, CNN, and RNN in network analysis.