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Convergent sequences of dense graphs II. Multiway cuts and statistical physics
by
Sós, V. T.
, Borgs, C.
, Vesztergombi, K.
, Lovász, L.
, Chayes, J. T.
in
Free energy
/ Graph theory
/ Ground state
/ Homomorphisms
/ Integers
/ Ising model
/ Magnetic fields
/ Perceptron convergence procedure
/ Quotients
/ Step functions
2012
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Convergent sequences of dense graphs II. Multiway cuts and statistical physics
by
Sós, V. T.
, Borgs, C.
, Vesztergombi, K.
, Lovász, L.
, Chayes, J. T.
in
Free energy
/ Graph theory
/ Ground state
/ Homomorphisms
/ Integers
/ Ising model
/ Magnetic fields
/ Perceptron convergence procedure
/ Quotients
/ Step functions
2012
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Do you wish to request the book?
Convergent sequences of dense graphs II. Multiway cuts and statistical physics
by
Sós, V. T.
, Borgs, C.
, Vesztergombi, K.
, Lovász, L.
, Chayes, J. T.
in
Free energy
/ Graph theory
/ Ground state
/ Homomorphisms
/ Integers
/ Ising model
/ Magnetic fields
/ Perceptron convergence procedure
/ Quotients
/ Step functions
2012
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Convergent sequences of dense graphs II. Multiway cuts and statistical physics
Journal Article
Convergent sequences of dense graphs II. Multiway cuts and statistical physics
2012
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Overview
We consider sequences of graphs (G n ) and define various notions of convergence related to these sequences including \"left-convergence,\" defined in terms of the densities of homomorphisms from small graphs into G n , and \"right-convergence,\" defined in terms of the densities of homomorphisms from G n into small graphs. We show that right-convergence is equivalent to left-convergence, both for simple graphs G n , and for graphs G n with nontrivial nodeweights and edgeweights. Other equivalent conditions for convergence are given in terms of fundamental notions from combinatorics, such as maximum cuts and Szemerédi partitions, and fundamental notions from statistical physics, like energies and free energies. We thereby relate local and global properties of graph sequences. Quantitative forms of these results express the relationships among different measures of similarity of large graphs.
Publisher
Dept. of Mathematics, Princeton University
Subject
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