Purpose: Despite intensive research during the last two decades, the detailed structural composition of the Internet is still opaque to researchers. Nevertheless, due to the importance of Internet maps for the development of more effective routing algorithms, security mechanisms, and resilience management, more detailed insights are required. This article advances the understanding of the Internet structure by integrating data from different large-scale measurement campaigns into a set of comprehensive Internet graphs at different abstraction levels, and analyzes them in terms of important statistics and graph measures. Design/methodology/approach: This study follows the topology measurement framework suggested by Gunes and Sarac (2009), involving three phases: topology collection, topology construction, and topology analysis. Findings: An integrated data set of Internet graphs at different abstraction layers is provided that can serve as a baseline for future research on Internet analytics. Furthermore, results of important graph metrics are presented and power-law relationships for the degree distributions on every level of the current Internet are substantiated. Research limitations/implications: By necessity, the integrated graphs provide a snapshot of the Internet topology. In future work, repeated measurements and automated data integration could lead to a better understanding of Internet dynamics. Practical implications: Due to increasing dependency on the Internet as a critical global infrastructure, studying Internet connectivity is more important than ever for both companies and Internet service providers. The data set will be made publically available for network research. Social implications: Understanding the structure of Internet serves as a fundamental step in improving the robustness, security, and privacy of any online service. Originality/value: By carefully integrating six different traceroute datasets such as iPlane, CAIDA, Carna, DIMES, RIPE Atlas, and RIPE IPv6L, this paper presents the Internet graphs of a substantially larger and thus solid scale than previously known, at well-established abstraction levels such as the IP interface, router, Point of Presence (PoP), Autonomous System (AS), and Internet Service Provider (ISP). Furthermore, by employing a broad diversity of graph measures, this study creates a more exhaustive snapshot of the global Internet topology than earlier works.
