article

Collecting the internet AS-level topology

Authors:

Beichuan Zhang,

Lixia ZhangAuthors Info & Claims

ACM SIGCOMM Computer Communication Review, Volume 35, Issue 1

Pages 53 - 61

https://doi.org/10.1145/1052812.1052825

Published: 01 January 2005 Publication History

Abstract

At the inter-domain level, the Internet topology can be represented by a graph with Autonomous Systems (ASes) as nodes and AS peerings as links. This AS-level topology graph has been widely used in a variety of research efforts. Conventionally this topology graph is derived from routing tables collected by Route Views or RIPE RIS. In this work, we assemble the most complete AS-level topology by extending the conventional method along two dimensions. First, in addition to using data from RouteViews and RIPE RIS, we also collect data from many other sources, including route servers, looking glasses, and routing registries. Second, in addition to using routing tables, we also accumulate topological information from routing updates over time. The resulting topology graph on a recent day contains 44% more links and 3% more nodes than that from using RouteViews routing tables alone. Our data collection and topology generation process have been automated, and we publish the latest topology on the web on a daily basis.

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Index Terms

Collecting the internet AS-level topology
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools
2. Networks
  1. Network properties
    1. Network structure
  2. Network protocols
    1. Network layer protocols
      1. Routing protocols

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Published In

cover image ACM SIGCOMM Computer Communication Review

ACM SIGCOMM Computer Communication Review Volume 35, Issue 1

January 2005

108 pages

ISSN:0146-4833

DOI:10.1145/1052812

Issue’s Table of Contents

Copyright © 2005 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2005

Published in SIGCOMM-CCR Volume 35, Issue 1

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Cited By

Gao HLi NXie Y(2024)Hidden-SAGE: For the Inference of Complex Autonomous System Business Relationships Involving Hidden LinksElectronics10.3390/electronics1309161713:9(1617)Online publication date: 23-Apr-2024
https://doi.org/10.3390/electronics13091617
Chen DZhao XXiao W(2024)Fine-Grained Anomaly Detection on Dynamic Graphs via Attention Alignment2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00246(3178-3190)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00246
Guan WZhang HGuan WZhang H(2024)Efficient Management of Physical Infrastructure NetworksNetwork Slicing for Future Wireless Communication10.1007/978-3-031-58229-5_2(13-35)Online publication date: 4-Apr-2024
https://doi.org/10.1007/978-3-031-58229-5_2
Sun CWang ZRui XYu PSun L(2023)An in-depth study on key nodes in social networksIntelligent Data Analysis10.3233/IDA-22701827:6(1811-1838)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/IDA-227018
Wang LFan JZhong FLiu YLiu J(2023)Anomaly Detection in Dynamic Networks through Edge-Tight Structure Embedding2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00098(644-651)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TrustCom60117.2023.00098
Li XZhou TCai ZSu J(2023)Realizing Fine-Grained Inference of AS Path With a Generative Measurable ProcessIEEE/ACM Transactions on Networking10.1109/TNET.2023.327056531:6(3112-3127)Online publication date: Dec-2023
https://doi.org/10.1109/TNET.2023.3270565
Guo JTang SLi JPan KWu L(2023)RustGraph: Robust Anomaly Detection in Dynamic Graphs by Jointly Learning Structural-Temporal DependencyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332864536:7(3472-3485)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3328645
Liu YPan SWang YXiong FWang LChen QLee V(2023)Anomaly Detection in Dynamic Graphs via TransformerIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312406135:12(12081-12094)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3124061
Gao ZMa LLi MZhao GLi M(2023)Anomaly Detection in Dynamic Graphs Via Long Short-Term Temporal Attention Network2023 International Conference on Machine Learning and Cybernetics (ICMLC)10.1109/ICMLC58545.2023.10327925(153-158)Online publication date: 9-Jul-2023
https://doi.org/10.1109/ICMLC58545.2023.10327925
Yang CJia W(2023)BGP anomaly detection - a path-based apporach2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)10.1109/ACCTCS58815.2023.00100(408-414)Online publication date: Feb-2023
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