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194
Convergent Sequences of Dense Graphs I: Subgraph Frequencies, Metric Properties and Testing
, 2006
"... We consider sequences of graphs (Gn) and define various notions of convergence related to these sequences: “left convergence” defined in terms of the densities of homomorphisms from small graphs into Gn; “right convergence” defined in terms of the densities of homomorphisms from Gn into small graphs ..."
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Cited by 98 (6 self)
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We consider sequences of graphs (Gn) and define various notions of convergence related to these sequences: “left convergence” defined in terms of the densities of homomorphisms from small graphs into Gn; “right convergence” defined in terms of the densities of homomorphisms from Gn into small graphs; and convergence in a suitably defined metric. In Part I of this series, we show that left convergence is equivalent to convergence in metric, both for simple graphs Gn, and for graphs Gn with nodeweights and edgeweights. One of the main steps here is the introduction of a cutdistance comparing graphs, not necessarily of the same size. We also show how these notions of convergence provide natural
Girth of Sparse Graphs
 2002), 194  200. ILWOO CHO
"... Recently, Bollobás, Janson and Riordan introduced a very general family of random graph models, producing inhomogeneous random graphs with Θ(n) edges. Roughly speaking, there is one model for each kernel, i.e., each symmetric measurable function from [0,1] 2 to the nonnegative reals, although the d ..."
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Cited by 76 (6 self)
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Recently, Bollobás, Janson and Riordan introduced a very general family of random graph models, producing inhomogeneous random graphs with Θ(n) edges. Roughly speaking, there is one model for each kernel, i.e., each symmetric measurable function from [0,1] 2 to the nonnegative reals, although the details are much more complicated, to ensure the exact inclusion of many of the recent models for largescale realworld networks. A different connection between kernels and random graphs arises in the recent work of Borgs, Chayes, Lovász, Sós, Szegedy and Vesztergombi. They introduced several natural metrics on dense graphs (graphs with n vertices and Θ(n 2) edges), showed that these metrics are equivalent, and gave a description of the completion of the space of all graphs with respect to any of these metrics in terms of graphons, which are essentially bounded kernels. One of the most appealing aspects of this work is the message that sequences of inhomogeneous quasirandom graphs are in a
Flag algebras
 JOURNAL OF SYMBOLIC LOGIC
"... Asymptotic extremal combinatorics deals with questions that in the language of model theory can be restated as follows. For finite models M, N of an universal theory without constants and function symbols (like graphs, digraphs or hypergraphs), let p(M, N) be the probability that a randomly chosen ..."
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Cited by 70 (6 self)
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Asymptotic extremal combinatorics deals with questions that in the language of model theory can be restated as follows. For finite models M, N of an universal theory without constants and function symbols (like graphs, digraphs or hypergraphs), let p(M, N) be the probability that a randomly chosen submodel of N with M  elements is isomorphic to M. Which asymptotic relations exist between the quantities p(M1, N),..., p(Mh, N), where M1,..., Mh are fixed “template ” models and N  grows to infinity? In this paper we develop a formal calculus that captures many standard arguments in the area, both previously known and apparently new. We give the first application of this formalism by presenting a new simple proof of a result by Fisher about the minimal possible density of triangles in a graph with given edge density.
Estimating and understanding exponential random graph models
, 2011
"... We introduce a new method for estimating the parameters of exponential random graph models. The method is based on a largedeviations approximation to the normalizing constant shown to be consistent using theory developed by Chatterjee and Varadhan [15]. The theory explains a host of difficulties e ..."
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Cited by 48 (1 self)
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We introduce a new method for estimating the parameters of exponential random graph models. The method is based on a largedeviations approximation to the normalizing constant shown to be consistent using theory developed by Chatterjee and Varadhan [15]. The theory explains a host of difficulties encountered by applied workers: many distinct models have essentially the same MLE, rendering the problems “practically” illposed. We give the first rigorous proofs of “degeneracy” observed in these models. Here, almost all graphs have essentially no edges or are essentially complete. We supplement recent work of Bhamidi, Bresler and Sly [6] showing that for many models, the extra sufficient statistics are useless: most realizations look like the results of a simple Erdős–Rényi model. We also find classes of models where the limiting graphs differ from Erdős–Rényi graphs and begin to make the link to models where the natural parameters alternate in sign.
Graph limits and exchangeable random graphs
, 2007
"... We develop a clear connection between deFinetti’s theorem for exchangeable arrays (work of Aldous–Hoover–Kallenberg) and the emerging area of graph limits (work of Lovász and many coauthors). Along the way, we translate the graph theory into more classical probability. ..."
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Cited by 47 (9 self)
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We develop a clear connection between deFinetti’s theorem for exchangeable arrays (work of Aldous–Hoover–Kallenberg) and the emerging area of graph limits (work of Lovász and many coauthors). Along the way, we translate the graph theory into more classical probability.
On exchangeable random variables and the statistics of large graphs and hypergraphs
, 2008
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The large deviation principle for the ErdösRenyi random graph
 EUROPEAN JOURNAL OF COMBINATORICS (SPECIAL ISSUE ON HOMOMORPHISMS AND LIMITS
, 2011
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Convergent Sequences of Dense Graphs II. Multiway Cuts and Statistical Physics
, 2007
"... We consider sequences of graphs (Gn) 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 Gn, and “right convergence, ” defined in terms of the densities of homomorphisms from Gn i ..."
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Cited by 39 (5 self)
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We consider sequences of graphs (Gn) 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 Gn, and “right convergence, ” defined in terms of the densities of homomorphisms from Gn into small graphs. We show that right convergence is equivalent to left convergence, both for simple graphs Gn, and for graphs Gn 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
ANALYSIS AND APPROXIMATION OF NONLOCAL DIFFUSION PROBLEMS WITH VOLUME CONSTRAINTS
, 2012
"... Abstract. A recently developed nonlocal vector calculus is exploited to provide a variational analysis for a general class of nonlocal diffusion problems described by a linear integral equation on bounded domains in Rn. The nonlocal vector calculus also enables striking analogies to be drawn between ..."
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Cited by 28 (7 self)
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Abstract. A recently developed nonlocal vector calculus is exploited to provide a variational analysis for a general class of nonlocal diffusion problems described by a linear integral equation on bounded domains in Rn. The nonlocal vector calculus also enables striking analogies to be drawn between the nonlocal model and classical models for diffusion, including a notion of nonlocal flux. The ubiquity of the nonlocal operator in applications is illustrated by a number of examples ranging from continuum mechanics to graph theory. In particular, it is shown that fractional Laplacian and fractional derivative models for anomalous diffusion are special cases of the nonlocal model for diffusion we consider. The numerous applications elucidate different interpretations of the operator and the associated governing equations. For example, a probabilistic perspective explains that the nonlocal spatial operator appearing in our model corresponds to the infinitesimal generator for a symmetric jump process. Sufficient conditions on the kernel of the nonlocal operator and the notion of volume constraints are shown to lead to a wellposed problem. Volume constraints are a proxy for boundary conditions that may not be defined for a given kernel. In particular, we demonstrate for a general class of kernels that the nonlocal operator is a mapping between a constrained subspace of a fractional Sobolev subspace and its dual. We also demonstrate for other particular kernels that the inverse of the operator does not smooth but does correspond to diffusion. The impact of our results is that both a continuum analysis and a numerical method for the modeling of anomalous diffusion on bounded domains in Rn are provided. The analytical framework allows us to consider finitedimensional approximations using both discontinuous or continuous Galerkin methods, both of which are conforming for the nonlocal diffusion equation we consider; error and condition number estimates are derived.