Why GNNs Outperform LLMs in Network Monitoring and Analysis

While Large Language Models (LLMs) are powerful tools for processing textual data, they lack the ability to analyze the topological and relational data critical to network operations. Here’s why GNNs are superior:

1. Topology Awareness
• GNNs: Naturally model the structure of networks, capturing relationships between devices and their connections.
• LLMs: Require graph data to be converted into textual descriptions, losing critical structural insights.

2. Real-Time Responsiveness
• GNNs: Process telemetry data in real time, enabling immediate detection of anomalies like packet loss or routing leaks.
• LLMs: Struggle with real-time data processing and require extensive preprocessing.

3. Root Cause Analysis
• GNNs: Pinpoint the exact source of network issues by analyzing relationships between events.
• LLMs: Depend on textual correlations, which are less precise and often require human interpretation.

4. Scalability
• GNNs: Handle millions of nodes and petabytes of data effortlessly, making them ideal for large-scale networks.
• LLMs: Struggle to scale efficiently for such use cases.

5. Operational Context
• GNNs: Provide actionable insights tailored to networking tasks, such as QoS misconfigurations or bandwidth congestion.
• LLMs: Lack the domain-specific focus needed for network operations.