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ICML
2005
IEEE
14 years 8 months ago
Learning from labeled and unlabeled data on a directed graph
We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is co...
Bernhard Schölkopf, Dengyong Zhou, Jiayuan Hu...
GFKL
2005
Springer
95views Data Mining» more  GFKL 2005»
14 years 1 months ago
An Indicator for the Number of Clusters: Using a Linear Map to Simplex Structure
Abstract. The problem of clustering data can be formulated as a graph partitioning problem. In this setting, spectral methods for obtaining optimal solutions have received a lot of...
Marcus Weber, Wasinee Rungsarityotin, Alexander Sc...
LION
2009
Springer
125views Optimization» more  LION 2009»
14 years 2 months ago
New Bounds on the Clique Number of Graphs Based on Spectral Hypergraph Theory
This work introduces new bounds on the clique number of graphs derived from a result due to S´os and Straus, which generalizes the Motzkin-Straus Theorem to a specific class of h...
Samuel Rota Bulò, Marcello Pelillo
ICML
2009
IEEE
14 years 8 months ago
Learning spectral graph transformations for link prediction
We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph's algebraic spectru...
Andreas Lommatzsch, Jérôme Kunegis
DISOPT
2008
93views more  DISOPT 2008»
13 years 7 months ago
Exploiting planarity in separation routines for the symmetric traveling salesman problem
At present, the most successful approach to solving large-scale instances of the Symmetric Traveling Salesman Problem to optimality is branch-and-cut. The success of branch-and-cu...
Adam N. Letchford, Nicholas A. Pearson