Network Tomography: Recent Developments
By Rui M. Castro, Mark J. Coates, Gang Liang, Robert D. Nowak, Bing Yu. 2004
482 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/c0389b33e2308e061687438564a35e27dc7a6029
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- David Firth
- Yong Ren
- JingJing Wang
- Lajos Hanzo
- Chunxiao Jiang
- Nicolai Meinshausen
- Haijun Zhang
- Kwang-Cheng Chen
In Statistical and Nonlinear Physics
- Massimo Tornatore
- Nicolai Meinshausen
- Irene Macaluso
- Cristina Rottondi
- Avishek Nag
- Darko Zibar
- Marco Ruffini
- Francesco Musumeci
Citations in the corpus
- Sign-constrained least squares estimation for high-dimensional regression (2012, background): “There are different aspects of network tomography, including origin-destimation matrix estimation and link-level network tomography; see Castro et al. (2004) for a good overview of the statistical aspects and Xi et al. (2006) and Lawrence et al. (2006) for a discussion of active tomography in the…”
- Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “Classic network tomography [364] defines the problem as: Y = AX, where X is a J-dimensional vector of the network’s dynamically fluctuating parameters, such as the link delay or traffic activity, Y is the I-dimensional vector of measurements and A is known as the routing matrix that is likely to def”
- Learning Graphs From Data: A Signal Representation Perspective (2018, unknown): “The field of network tomography broadly concerns methods for inferring properties of networks from indirect observations [29].”
- AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS (2011, methodology): “Chandler and Bate (2007) propose a different type of adjustment for the independence likelihood: essentially stretching the composite log-likelihood on the θ-axis about θ̂CL to ensure, at least approximately, that the second Bartlett identity holds, and thus that the usual χ(2)q approximation can be”
- An Overview on Application of Machine Learning Techniques in Optical Networks (2018, methodology): “Two alternative approaches, namely network kriging7 (first described in [107]) and norm L2 minimization (typically used in network tomography [108]), are applied in [36] and [37]”