C. Gkantsidis, M. Mihail, and E. Zegura. Spectral Analysis of Internet Topologies. In Proceedings of Infocom '03, pages 364--374, San Francisco, CA, March -- April 2003. Home/Search Document Details and Download
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On the Emergence of Highly Variable Distributions.. - Fayed, Krapivsky, .. (2003) (1 citation) (Correct)
....AS (a strong assumption of global knowledge) the B A model strongly constrains the resulting connection pattern. This is restrictive; as discussed in [26] many graph realizations are consistent with a given degree sequence, and different realizations may have very different properties. In fact, [25] shows that the AS graph exhibits a high degree of clustering, an effect that is not captured by the particular connection pattern created by the B A model. In contrast, the assumption in our model is that AS sizes are the underlying cause of high variability, and that a large AS will naturally ....
M. Mihail, C. Gkantsidis, and E. Zegura. Spectral analysis of Internet topologies. In Proceedings of Infocom 2003.
On the Emergence of Highly Variable Distributions.. - Fayed, Krapivsky, .. (2003) (1 citation) (Correct)
....AS (a strong assumption of global knowledge) the B A model strongly constrains the resulting connection pattern. This is restrictive; as discussed in [26] many graph realizations are consistent with a given degree sequence, and different realizations may have very different properties. In fact, [25] shows that the AS graph exhibits a high degree of clustering, an effect that is not captured by the particular connection pattern created by the B A model. In contrast, the assumption in our model is that AS sizes are the underlying cause of high variability, and that a large AS will naturally ....
M. Mihail, C. Gkantsidis, and E. Zegura. Spectral analysis of Internet topologies. In Proceedings of Infocom 2003.
Random Walks in Peer-to-Peer Networks - Gkantsidis, Mihail, Saberi (2004) (13 citations) Self-citation (Gkantsidis Mihail) (Correct)
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C. Gkantsidis, M. Mihail, and E. Zegura, "Spectral analysis of internet topologies," in Proc. Infocom. IEEE, 2003.
Random Walks in Peer-to-Peer Networks - Gkantsidis, Mihail, Saberi (2004) (13 citations) Self-citation (Gkantsidis Mihail) (Correct)
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C. Gkantsidis, M. Mihail, and E. Zegura, "Spectral analysis of internet topologies," in Proc. Infocom. IEEE, 2003.
Random Walks in Peer-to-Peer Networks - Christos Gkantsidis Milena (2004) (13 citations) Self-citation (Gkantsidis Mihail) (Correct)
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C. Gkantsidis, M. Mihail, and E. Zegura, "Spectral analysis of internet topologies," in Proc. Infocom. IEEE, 2003.
Conductance and Congestion in Power Law Graphs - Gkantsidis, Mihail, Saberi (2003) (5 citations) Self-citation (Gkantsidis Mihail) (Correct)
....log n factor o# from the congestion achieved by linear size regular graphs with constant expansion. Thus our result can be understood as follows. The skewed degree distributions of PLRGs result in a hierarchical organization, with nodes of (typically) high degree forming the core of the network [53, 54, 26]. This is reminiscent of a tree like structure. Intuitively, we expect that links in the core carry more flow. Our result suggests that the bound O(n log by which the flow scales in the core of PLRGs is closer to the bound O(n log n) of a robust flat structure, such as an expander, rather than ....
....for the model of growth with preferential attachment [40] In passing, we also note that the rather sharp bounds obtained in Corollary 3. 4 are further evidence that the spectrum of the stochastic normalization of the adjacency matrix is an important metric for Internet topologies (see also [21, 39, 26]) The balance of the paper is as follows: In Section 2 we discuss random graph models for graphs with skewed degrees, including Internet topologies, and formalize the random model that is suitable for our study. In Section 3 we give a theoretical argument based on conductance and along the ....
C. Gkantsidis, M. Mihail, and E. Zegura. Spectral analysis of internet topologies. In Proc. Infocom. IEEE, 2003. To appear.
Characteristic Timescales: Analyzing End-to-end Delay - In Networks Under (Correct)
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C. Gkantsidis, M. Mihail, and E. Zegura. Spectral Analysis of Internet Topologies. In Proceedings of Infocom '03, pages 364--374, San Francisco, CA, March -- April 2003.
SACA: SCM-based Adaptive Clustering Algorithm - Li, Verma, Lao, Cui (2005) (Correct)
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C. Gkantsidis, M. Mihail, , and E. Zegura. Spectral analysis of internet topologies. In IEEE INFOCOM, October 27-29, 2003.
Reducing Large Internet Topologies - For Faster Simulations (2005) (Correct)
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C. Gkantsidis, M. Mihail, and E. Zegura. Spectral analysis of Internet topologies. IEEE INFOCOM, 2003.
Efficient Algorithms for Sampling and Clustering of Large.. - Orponen, Schaeffer (Correct)
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C. Gkantsidis, M. Mihail, and E. Zegura. Spectral analysis of Internet topologies. In Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'03), pages 364--374, New York, NY, 2003. IEEE.
On the Emergence of Highly Variable Distributions in - System (Correct)
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M. Mihail, C. Gkantsidis, and E. Zegura. Spectral analysis of Internet topologies. In Proceedings of IEEE INFOCOM, April 2003.
On the Emergence of Highly Variable Distributions.. - Fayed, Krapivsky, .. (2003) (1 citation) (Correct)
No context found.
M. Mihail, C. Gkantsidis, and E. Zegura. Spectral analysis of Internet topologies. In Proceedings of IEEE INFOCOM, April 2003.
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