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» Randomization Techniques for Graphs.
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168
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ICML
2003
IEEE
16 years 1 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
102
Voted
RSA
2010
99views more  RSA 2010»
14 years 11 months ago
Hamiltonicity thresholds in Achlioptas processes
In this paper we analyze the appearance of a Hamilton cycle in the following random process. The process starts with an empty graph on n labeled vertices. At each round we are pre...
Michael Krivelevich, Eyal Lubetzky, Benny Sudakov
105
Voted
SODA
2008
ACM
105views Algorithms» more  SODA 2008»
15 years 2 months ago
Deterministic random walks on regular trees
Jim Propp's rotor router model is a deterministic analogue of a random walk on a graph. Instead of distributing chips randomly, each vertex serves its neighbors in a fixed or...
Joshua N. Cooper, Benjamin Doerr, Tobias Friedrich...
119
Voted
JGAA
2007
123views more  JGAA 2007»
15 years 15 days ago
A Polynomial Time Algorithm for Finding Nash Equilibria in Planar Win-Lose Games
Abstract. Two-player win-lose games have a simple directed graph representation. Exploiting this, we develop graph theoretic techniques for finding Nash equilibria in such games. ...
Louigi Addario-Berry, Neil Olver, Adrian Vetta
128
Voted
CCR
2004
89views more  CCR 2004»
15 years 14 days ago
Should we build Gnutella on a structured overlay?
There has been much interest in both unstructured and structured overlays recently. Unstructured overlays, like Gnutella, build a random graph and use flooding or random walks on ...
Miguel Castro, Manuel Costa, Antony I. T. Rowstron