This paper introduces a novel deep-learning based generator of synthetic graphs that represent intra-Autonomous System (AS) 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 Internet Topology Data Kit (ITDK), called Internet Graphs (IGraphs). To create IGraphs, the Filtered Recurrent Multi-level (FRM) algorithm for community extraction was developed. It is shown that DGGI creates synthetic graphs which accurately reproduce the properties of centrality, clustering, assortativity, and node degree. The DGGI generator overperforms existing Internet topology generators. On average, DGGI improves the Maximum Mean Discrepancy (MMD) metric 84.4%, 95.1%, 97.9%, and 94.7% for assortativity, betweenness, clustering, and node degree, respectively.

翻译：本文提出了一种名为Deep-generative graphs for the Internet (DGGI)的新型深度学习生成器，用于生成代表互联网中自治域内拓扑的合成图。同时，本文还介绍了一个从互联网拓扑数据包（ITDK）项目中提取的真实自治域内图的大规模新型数据集，称为Internet Graphs (IGraphs)。为构建IGraphs，我们开发了用于社区提取的过滤递归多层级（FRM）算法。结果表明，DGGI生成的合成图能够精确复现中心性、聚类系数、同配性和节点度等拓扑属性。DGGI生成器在性能上超越了现有互联网拓扑生成器。平均而言，DGGI在同配性、介数中心性、聚类系数和节点度四个方面分别将最大均值差异（MMD）指标提升了84.4%、95.1%、97.9%和94.7%。