Large Language Model (LLM)-enabled Graphs in Dynamic Networking

Geng Sun, Yixian Wang, Dusit Niyato, Jiacheng Wang, Xinying Wang, H. Vincent Poor, Khaled B. Letaief

Abstract

Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas involving networks. In this article, we explore an integration of LLMs and graphs in dynamic networks, focusing on potential applications and a practical study. Specifically, we first review essential technologies and applications of LLM-enabled graphs, followed by an exploration of their advantages in dynamic networking. Subsequently, we introduce and analyze LLM-enabled graphs and their applications in dynamic networks from the perspective of LLMs as different roles. On this basis, we propose a novel framework of LLM-enabled graphs for networking optimization, and then present a case study on UAV networking, concentrating on optimizing UAV trajectory and communication resource allocation to validate the effectiveness of the proposed framework. Finally, we outline several potential future extensions.

The Proposed Framework for LLM-enabled graphs.

Our proposed framework follows a layered architecture, which consists of five layers.
(Input Layer): The input layer receives requests that can represent information related to a graph in a dynamic network, including a control node (e.g., a UAV operator), transmission nodes (e.g., UAVs), and data nodes that need to be processed by UAVs, allowing multi-modal data as input, such as text, images, video, and audio.
(Graph-to-Text Layer): In the graph-to-text layer, we first use prompt engineering to optimize the graph information received from the input layer and extract the most representative and information-rich features. Then, the graph information is transformed into vector representations that the system can understand and process through embeddings to facilitate downstream model processing such as text generation or graph classification models. Finally, numerical features in the vector are decoded into understandable semantic features to generate the corresponding text description.
(Decision Layer): The decision layer employs a suitable LLM such as Bing Chat, Chatgpt4, or Bard to make decisions. Specifically, the LLM analyzes and processes the text from the graph to text layer, combines the contextual information of the input text for inference and prediction, and generates new text as output to support subsequent task execution.
(Text-to-Graph Layer): In the text-to-graph layer, the key features of nodes and edges are extracted from the text generated by the LLM to construct the graph structure. Then, the graph structure is processed by a GNN, and the information of nodes, edges and text attributes are embedded. Finally, graph and text embeddings are merged to form an integrated vector representation for downstream tasks.
(Output Layer): The output layer will verify, analyze, monitor and present the results of dynamic network graphs, and interact with users, making the complex graph data easier to understand and utilize.

The Experiment Result of LLM-enabled graphs.

This figure shows experimental system performance results for UAV optimization. (a) The difference between measurements and estimates. (b) The impact of task size at monitoring points on the UAV's remaining energy consumption.
We can observe that the gap between the estimated and measured values decreases as the number of training episodes increases. It is seen that our proposed LLM+GNN outperforms LLM+Node2Vec and LLM+GAT.
Moreover, as the amount of data collected (i.e., task size) at monitoring points increases, the remaining energy consumption of the UAV gradually decreases. This is because the UAV needs to adjust its flight trajectory and allocate more resources to the monitoring points with larger tasks, which results in the increased energy consumption. This enables the UAV to gather more information and execute environmental monitoring operations accurately. Our proposed LLM+GNN performs more stably and reliably under various task sizes, which means that it is especially suitable for heavily loaded and resource-limited UAV systems. The reason for this lies in the fact that LLM+GAT, while effective at dynamically allocating resources based on node importance, faces challenges with over-allocation as task sizes increase. Furthermore, LLM+Node2Vec performs well only at a task size of 5 MB, leveraging its ability to capture local and global network structures, optimize node proximity, and extract key information efficiently.

Run the Program

1) Create a new conda environment with the following command:


      conda create --name LLMGNN python==3.9

2) Activate the created environment with the following command:


      conda activate LLMGNN

3) Install the following packets using pip:


      pip install torch==1.13.1
      pip install networkx==3.2.1
      pip install torch-geometric==2.5.3
      pip install torch_cluster==1.6.0
      pip install torch_scatter==2.1.0
      pip install torch_sparse==0.6.15
      pip install torch_spline_conv==1.2.1
      pip install matplotlib==3.9.0
      pip install numpy==1.23.0

4) Run the different algorithm:


      LLM+GNN: run LLM_GNN.py;
      LLM+GAT: run LLM_GAT.py;
      LLM+Node2Vec: run LLM_Node2Vec.py;

BibTeX

@article{sun2024,
        title={Large Language Model (LLM)-enabled Graphs in Dynamic Networking},
        author={Geng Sun, Yixian Wang, Dusit Niyato, Jiacheng Wang, Xinying Wang, H. Vincent Poor, Khaled B. Letaief},
        journal={},
        year={2024}
      }