Data-driven Intra-Autonomous Systems Graph Generator

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.