Sampling biases in IP topology measurements

By Anukool Lakhina, John W. Byers, Mark Crovella, Peng Xie. IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), 2003

351 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1701f527492beefd6dd9e37e1403126c52e2bbce

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Citations in the corpus

The internet AS-level topology: three data sources and one definitive metric (2005, background): “[20] numerically explored sampling biases arising from traceroute measurements and found that such traceroute-sampled graphs of the Internet yield insufficient evidence for characterizing the actual un-”
Random Dot Product Graph Models for Social Networks (2007, background): “Due to the size of these networks [3] and the difficulty of determining complete link information [4, 5] a significant amount of research has gone into finding models that match observed properties of these graphs in order to empirically (via simulation) and theoretically understand and predict prop”
Collecting the internet AS-level topology (2005, methodology): “These conclusions in [12] and [4] do not directly apply to our work because (1) in BGP almost all nodes appear as destinations, i.”
DIMES: let the internet measure itself (2005, methodology): “At the coarse level, where each node is an AS, there are several mapping efforts, most notably are the acti ve measurement Skitter project [5] and the passive collection of BGP data done by the RouteViews project [10], but also many of the studies mentioned above [1], [3], [6], [8], [9], [4], [7] ex”
Random Graphs and Complex Networks (2016, background): “In [178], it is argued that traceroute-measurements, by which the Internet-topology is uncovered, could be partially responsible for the fact that power-law degree sequences are observed in Internet.”
Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications (2005, background): “Yet another difficulty when dealing with traceroute-derived measurements has been considered in [58, 1] and concerns a potential bias whereby IP-level connectivity is inferred more easily and accurately the closer the routers are to the traceroute source(s).”
Correcting for missing data in information cascades (2011, background): “Related to our work here are the works that study the effects of missing data on measured properties of social networks [11] and the study of biases when obtaining a graph of the Internet based on measurements [13, 1].”
Potential networks, contagious communities, and understanding social network structure (2013, background): “Similarly, by datamining contagious networks, one expects to find attributes that are common amongst contagious networks; however, these observations may not apply to the underlying social network.”