A Multilayer Graph Model of the Internet Topology - Dataset (2012)
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Content: This dataset contains an integrated snapshot of the Internet Topology (year 2012) with corresponding graph models at the IP, Router, PoP, AS and ISP layers. 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.
数据集内容：本数据集收录了2012年互联网拓扑（Internet Topology）的整合快照，以及对应于IP接口、路由器（Router）、接入点（Point of Presence, PoP）、自治系统（Autonomous System, AS）与互联网服务提供商（Internet Service Provider, ISP）层级的图模型。 研究目的：尽管近二十年来学界开展了大量相关研究，但研究者仍无法洞悉互联网的详细结构组成。鉴于互联网拓扑图对开发更高效的路由算法、安全机制与韧性管理方案具有重要价值，学界亟需获取更为细致的互联网结构洞察。本研究通过整合多组大规模测量实验所得数据，构建了不同抽象层级的完整互联网图集合，并基于关键统计指标与图论度量展开分析，从而加深了学界对互联网结构的认知。 设计/研究方法：本研究遵循Gunes与Sarac（2009）提出的拓扑测量框架，涵盖三个核心阶段：拓扑数据采集、拓扑模型构建与拓扑分析。 研究发现：本研究提供了不同抽象层级的互联网图整合数据集，可作为未来互联网分析研究的基准参照。此外，本研究还给出了关键图论度量的计算结果，并验证了当前互联网各层级度分布的幂律（power-law）关系。 研究局限与启示：受客观条件限制，本整合数据集仅为2012年互联网拓扑的静态快照。未来可通过开展重复测量与自动化数据整合，进一步深化对互联网动态特性的认知。 实际应用启示：随着全球对作为关键基础设施的互联网依赖度与日俱增，无论是企业还是互联网服务提供商，研究互联网连通性的重要性都达到了前所未有的高度。本数据集将面向网络研究领域公开共享。 社会影响：明晰互联网结构，是提升各类在线服务鲁棒性、安全性与隐私保护水平的基础性步骤。 原创性与价值：本研究通过整合iPlane、CAIDA、Carna、DIMES、RIPE Atlas以及RIPE IPv6L等6组不同的路由追踪（traceroute）数据集，在IP接口、路由器、接入点（PoP）、自治系统（AS）与互联网服务提供商（ISP）等成熟的抽象层级上，构建了规模远超以往已知成果的可靠互联网图集合。此外，本研究通过采用丰富多样的图论度量方法，生成了比此前同类研究更为全面的全球互联网拓扑快照。
