Data-driven Intra-Autonomous Systems Graph Generator

TL;DR

This paper presents DGGI, a deep learning-based generator for realistic intra-AS Internet topologies, outperforming existing models by accurately reproducing key graph properties and providing a new dataset for research.

Contribution

It introduces DGGI, a novel deep-learning graph generator for intra-AS topologies, and a large dataset of real intra-AS graphs, addressing limitations of previous models.

Findings

DGGI significantly outperforms existing generators in reproducing graph properties.

DGGI improves the MMD metric by over 84% for key properties.

The dataset IGraphs provides a valuable resource for Internet topology research.

Abstract

Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication networks, emphasizing the need for advanced topology generators encompassing diverse Internet topology characteristics. This paper introduces a novel deep-learning-based generator of synthetic graphs representing intra-autonomous in the Internet, named Deep-Generative Graphs for the Internet (DGGI). It also presents a novel massive dataset of real intra-AS graphs extracted from the project ITDK, called IGraphs. It is shown that DGGI creates synthetic graphs that accurately reproduce the properties of centrality, clustering, assortativity, and node degree. The DGGI generator overperforms existing Internet topology generators. On average, DGGI improves the MMD metric $84.4\%$, $95.1\%$, $97.9\%$, and $94.7\%$ for assortativity, betweenness, clustering, and node degree, respectively.