  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.
