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» Graph parameters and semigroup functions
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ICML
2007
IEEE
14 years 5 months ago
Parameter learning for relational Bayesian networks
We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
Manfred Jaeger
STOC
2010
ACM
269views Algorithms» more  STOC 2010»
13 years 9 months ago
Approximations for the Isoperimetric and Spectral Profile of Graphs and Related Parameters
The spectral profile of a graph is a natural generalization of the classical notion of its Rayleigh quotient. Roughly speaking, given a graph G, for each 0 < < 1, the spect...
Prasad Raghavendra, David Steurer and Prasad Tetal...
ISCI
2007
170views more  ISCI 2007»
13 years 4 months ago
Automatic learning of cost functions for graph edit distance
Graph matching and graph edit distance have become important tools in structural pattern recognition. The graph edit distance concept allows us to measure the structural similarit...
Michel Neuhaus, Horst Bunke
STOC
2000
ACM
112views Algorithms» more  STOC 2000»
13 years 9 months ago
A random graph model for massive graphs
We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize ...
William Aiello, Fan R. K. Chung, Linyuan Lu
AE
2007
Springer
13 years 11 months ago
A Study of Evaluation Functions for the Graph K-Coloring Problem
The evaluation or fitness function is a key component of any heuristic search algorithm. This paper introduces a new evaluation function for the well-known graph K-coloring proble...
Daniel Cosmin Porumbel, Jin-Kao Hao, Pascale Kuntz