Sciweavers

TCS
2010
13 years 9 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»
13 years 11 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»
13 years 11 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
14 years 19 days 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
14 years 4 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
14 years 5 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
15 years 9 months ago
Graph Theory with Applications
A classic book on graph theory.
J.A. Bondy and U.S.R. Murty

Book
5396views
15 years 10 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