Analyzing the Traffic of MANETs using Graph Neural Networks

TL;DR

This paper explores the application of Graph Neural Networks, specifically GraphSAGE, to analyze traffic in Mobile Ad-Hoc Networks, demonstrating promising link prediction accuracy and filling a research gap.

Contribution

It introduces a MANET dataset in PyTorch Geometric and evaluates GNNs' effectiveness for traffic analysis in MANETs, a novel application area.

Findings

GraphSAGE achieved 82.1% accuracy in link prediction.

First study to evaluate GNNs on MANET traffic analysis.

Provides a framework for future research in GNN-based MANET analysis.

Abstract

Graph Neural Networks (GNNs) have been taking role in many areas, thanks to their expressive power on graph-structured data. On the other hand, Mobile Ad-Hoc Networks (MANETs) are gaining attention as network technologies have been taken to the 5G level. However, there is no study that evaluates the efficiency of GNNs on MANETs. In this study, we aim to fill this absence by implementing a MANET dataset in a popular GNN framework, i.e., PyTorch Geometric; and show how GNNs can be utilized to analyze the traffic of MANETs. We operate an edge prediction task on the dataset with GraphSAGE (SAG) model, where SAG model tries to predict whether there is a link between two nodes. We construe several evaluation metrics to measure the performance and efficiency of GNNs on MANETs. SAG model showed 82.1 accuracy on average in the experiments.