Random graph

Results: 735



#Item
41Detecting malicious behavior in network and endpoint logs is an extremely challenging task: large and complex data sets, highly dynamic innocuous behavior, and intelligent adversaries contribute to the difficulty. Even a

Detecting malicious behavior in network and endpoint logs is an extremely challenging task: large and complex data sets, highly dynamic innocuous behavior, and intelligent adversaries contribute to the difficulty. Even a

Add to Reading List

Source URL: mmds-data.org

Language: English - Date: 2016-06-23 15:50:48
42Marginals-to-Models Reducibility  Michael Kearns University of Pennsylvania

Marginals-to-Models Reducibility Michael Kearns University of Pennsylvania

Add to Reading List

Source URL: theory.stanford.edu

Language: English - Date: 2013-11-08 17:46:03
43epl draft  Scale-free networks as an epiphenomenon of memory F. Caravelli1 , A. Hamma2 and M. Di Ventra3  arXiv:1312.2289v5 [physics.soc-ph] 15 Feb 2015

epl draft Scale-free networks as an epiphenomenon of memory F. Caravelli1 , A. Hamma2 and M. Di Ventra3 arXiv:1312.2289v5 [physics.soc-ph] 15 Feb 2015

Add to Reading List

Source URL: arxiv.org

Language: English - Date: 2015-02-16 21:04:27
44Network Analysis and Modeling, CSCI 5352 LectureProf. Aaron Clauset

Network Analysis and Modeling, CSCI 5352 LectureProf. Aaron Clauset

Add to Reading List

Source URL: tuvalu.santafe.edu

Language: English - Date: 2013-11-23 11:09:17
45Detecting malicious behavior in network and endpoint logs is an extremely challenging task: large and complex data sets, highly dynamic innocuous behavior, and intelligent adversaries contribute to the difficulty. Even a

Detecting malicious behavior in network and endpoint logs is an extremely challenging task: large and complex data sets, highly dynamic innocuous behavior, and intelligent adversaries contribute to the difficulty. Even a

Add to Reading List

Source URL: mmds-data.org

Language: English - Date: 2016-06-23 15:50:48
46CS264: Beyond Worst-Case Analysis Lecture #10: Planted and Semi-Random Graph Models∗ Tim Roughgarden† October 22,

CS264: Beyond Worst-Case Analysis Lecture #10: Planted and Semi-Random Graph Models∗ Tim Roughgarden† October 22,

Add to Reading List

Source URL: theory.stanford.edu

Language: English - Date: 2015-01-05 12:59:35
47The scaling window for a random graph with a given degree sequence Hamed Hatami and Michael Molloy∗ Department of Computer Science University of Toronto e-mail: ,

The scaling window for a random graph with a given degree sequence Hamed Hatami and Michael Molloy∗ Department of Computer Science University of Toronto e-mail: ,

Add to Reading List

Source URL: www.cs.toronto.edu

Language: English - Date: 2011-11-23 23:07:53
48Typical distances in a geometric model for complex networks ∗ Mohammed Amin Abdullah† Michel Bode‡

Typical distances in a geometric model for complex networks ∗ Mohammed Amin Abdullah† Michel Bode‡

Add to Reading List

Source URL: web.mat.bham.ac.uk

Language: English - Date: 2015-06-25 12:56:44
49De-anonymization of Heterogeneous Random Graphs in Quasilinear Time (extended abstract) Karl Bringmann1 , Tobias Friedrich2 , and Anton Krohmer2 1

De-anonymization of Heterogeneous Random Graphs in Quasilinear Time (extended abstract) Karl Bringmann1 , Tobias Friedrich2 , and Anton Krohmer2 1

Add to Reading List

Source URL: people.mpi-inf.mpg.de

Language: English - Date: 2016-01-03 06:46:27
50Spectral Graph Theory and Applications  WSLecture 7: Hitting Time and Cover Time of Random Walks Lecturer: Thomas Sauerwald & He Sun

Spectral Graph Theory and Applications WSLecture 7: Hitting Time and Cover Time of Random Walks Lecturer: Thomas Sauerwald & He Sun

Add to Reading List

Source URL: resources.mpi-inf.mpg.de

Language: English - Date: 2011-12-08 09:45:37