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TCS
2010
8 years 2 months ago
Clustering with partial information
The Correlation Clustering problem, also known as the Cluster Editing problem, seeks to edit a given graph by adding and deleting edges to obtain a collection of disconnected cliq...
Hans L. Bodlaender, Michael R. Fellows, Pinar Hegg...
JCT
2007
119views more  JCT 2007»
8 years 4 months ago
Cliques and the spectral radius
We prove a number of relations between the number of cliques of a graph G and the largest eigenvalue (G) of its adjacency matrix. In particular, writing ks (G) for the number of s...
Béla Bollobás, Vladimir Nikiforov
ENDM
2008
118views more  ENDM 2008»
8 years 4 months ago
Partial characterizations of clique-perfect and coordinated graphs: superclasses of triangle-free graphs
A graph G is clique-perfect if the cardinality of a maximum clique-independent set of H equals the cardinality of a minimum clique-transversal of H, for every induced subgraph H o...
Flavia Bonomo, Guillermo Durán, Francisco J...
CATS
2008
8 years 6 months ago
Parameterized Complexity of the Clique Partition Problem
The problem of deciding whether the edge-set of a given graph can be partitioned into at most k cliques is well known to be NP-complete. In this paper we investigate this problem ...
Egbert Mujuni, Frances A. Rosamond
ILP
2003
Springer
8 years 9 months ago
On Condensation of a Clause
In this paper, we investigate condensation of a clause. First, we extend a substitution graph introduced by Scheffer et al. (1996) to a total matcher graph. Then, we give a correc...
Kouichi Hirata
IPPS
2008
IEEE
8 years 11 months ago
Junction tree decomposition for parallel exact inference
We present a junction tree decomposition based algorithm for parallel exact inference. This is a novel parallel exact inference method for evidence propagation in an arbitrary jun...
Yinglong Xia, Viktor K. Prasanna

Book
1197views
10 years 2 months ago
Graph Theory with Applications
A classic book on graph theory.
J.A. Bondy and U.S.R. Murty

Book
5396views
10 years 3 months ago
Markov Random Field Modeling in Computer Vision
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms sy...
Stan Z. Li
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