Abstract
Ramsey's Theorem states that for every graph H, there is an integer R(H) such that every 2-edge-coloring of R(H)-vertex complete graph contains a monochromatic copy of H. In this talk, we focus on a natural quantitative extension: how many monochromatic copies of H can we find in every 2-edge-coloring of K_N, and what graphs H are so-called common, i.e., the number of monochromatic copies of H is asymptotically minimized by a random 2-edge-coloring. A classical result of Goodman from 1959 states that the triangle is a common graph. On the other hand, Thomason proved in 1989 that no clique of order at least four is common, and the existence of a common graph with chromatic number larger than three was open until 2012, when Hatami, Hladky, Kral, Norin and Razborov proved that the 5-wheel is common. In this talk, we show that for every k>4 there exists a common graph with chromatic number k.
This is a joint work with D. Kral and F. Wei
Abstract
Motifs (patterns of subgraphs), such as edges and triangles, encode important structural information about the geometry of a network. Consequently, counting motifs in a large network is an important statistical and computational problem. In this talk we will consider the problem of estimating motif densities and fluctuations of subgraph counts in an inhomogeneous random graph sampled from a graphon. We will show that the limiting distributions of subgraph counts can be Gaussian or non-Gaussian, depending on a notion of regularity of subgraphs with respect to the graphon. Using these results and a novel multiplier bootstrap for graphons, we will construct joint confidence sets for the motif densities. Finally, we will discuss various structure theorems and open questions about degeneracies of the limiting distribution.
Joint work with Anirban Chatterjee, Soham Dan, and Svante Janson.
Abstract
We initiate a study of large deviations for block model random graphs in the dense regime. Following Chatterjee and Varadhan (2011), we establish an LDP for dense block models, viewed as random graphons. As an application of our result, we study upper tail large deviations for homomorphism densities of regular graphs. We identify the existence of a "symmetric'' phase, where the graph, conditioned on the rare event, looks like a block model with the same block sizes as the generating graphon. In specific examples, we also identify the existence of a "symmetry breaking'' regime, where the conditional structure is not a block model with compatible dimensions. This identifies a "reentrant phase transition'' phenomenon for this problem---analogous to one established for Erdős–Rényi random graphs (Chatterjee and Dey (2010), Charrerjee and Varadhan (2011)). Finally, extending the analysis of Lubetzky and Zhao (2015), we identify the precise boundary between the symmetry and symmetry breaking regimes for homomorphism densities of regular graphs and the operator norm on Erdős–Rényi bipartite graphs.
Joint work with Christian Borgs, Jennifer Chayes, Subhabrata Sen, and Samantha Petti
Abstract
Variational approximations provide an attractive computational alternative to MCMC-based strategies for approximating the posterior distribution in Bayesian inference. Despite their popularity in applications, supporting theoretical guarantees are limited, particularly in high-dimensional settings. In this talk, we will study bayesian inference in the context of a linear model with product priors, and derive sufficient conditions for the correctness (to leading order) of the naive mean-field approximation. To this end, we will utilize recent advances in the theory of non-linear large deviations (Chatterjee and Dembo 2014). Next, we analyze the naive mean-field variational problem, and precisely characterize the asymptotic properties of the posterior distribution in this setting. The theory of graph limits provides a crucial ingredient to study this high-dimensional variational problem. This is based on joint work with Sumit Mukherjee (Columbia University).
Abstract
We discuss recent theorems on both smooth and singular responses of large dense graphs to changes in edge and triangle density constraints. Smoothness requires control over typical (exponentially most) graphs with given sharp values of those two densities.
In particular we prove the existence of a connected open set S in the plane of edge and triangle densities, cut into two pieces S' and S" by the curve C corresponding to graphs with independent edges. For typical graphs G with given edge and triangle densities, every subgraph density of G is real analytic on S' and S" as a function of the edge and triangle densities. However these subgraph densities are not analytic, or even differentiable, on C.
Joint work with Joe Neeman and Lorenzo Sadun.
Abstract
In the theory of random graphs the giant component has remained a guiding theme since the seminal paper of Erdos and Renyi. It has long been observed that the emergence of the giant component is analogous to the survival of a Galton-Watson branching process. This analogy crystallises the interplay between the local and the global structure of a sparse random graph. In fact, the notion that the Galton-Watson tree is the limiting object of the 'local structure' of the Erdos and Renyi random graph can be formalised nicely in the language of 'local weak convergence' introduced by Benjamini and Schramm and by Aldous and Steele. The local structure and local weak limits found their applications also in message passing algorithms, such as Belief Propagation and Warning Propagation, to mention a few.
Turning our attention to a random graph with a topological constraint (e.g., a random planar graph) where the independence of edges is lost, we will show that the planarity constraint affects the global component structure substantially, which in turn affects the local structure.
Abstract
A combinatorial structure is said to be quasirandom if it resembles a random structure in a certain robust sense. The notion of quasirandom graphs, developed in the work of Rödl, Thomason, Chung, Graham and Wilson in 1980s, is particularly robust as several different properties of truly random graphs, e.g., subgraph density, edge distribution and spectral properties, are satisfied by a large graph if and only if one of them is. We will discuss recent results on quasirandomness of various kinds of combinatorial structures (in particular, directed graphs, permutations and Latin squares) obtained using analytic tools provided by the theory of combinatorial limits.
Abstract
In this talk we study the random cluster model on essentially large girth and random regular graphs. We give explicit formula for the limiting free entropy of the random cluster model. Our result extends the work of Dembo, Montanari, Sly and Sun for the Potts model, and we prove a conjecture of Helmuth, Jenssen and Perkins about the phase transition of the random cluster model. This is joint work with Ferenc Bencs and Márton Borbényi.
The design of experiments involves an inescapable compromise between covariate balance and robustness. In this talk, we describe a formalization of this trade-off and introduce a new style of experimental design that allows experimenters to navigate it. The design is specified by a robustness parameter that bounds the worst-case mean squared error of an estimator of the average treatment effect. Subject to the experimenter’s desired level of robustness, the design aims to simultaneously balance all linear functions of potentially many covariates. The achieved level of balance is better than previously known possible, considerably better than what a fully random assignment would produce, and close to optimal given the desired level of robustness. We show that the mean squared error of the estimator is bounded by the minimum of the loss function of an implicit ridge regression of the potential outcomes on the covariates. The estimator does not itself conduct covariate adjustment, so one can interpret the approach as regression adjustment by design. Finally, we provide non-asymptotic tail bounds for the estimator, which facilitate the construction of conservative confidence intervals.
A well-known limitation of modeling causal systems via DAGs is their inability to encode context-specific information. Among the several proposed representations for context-specific causal information are the staged tree models, which are colored probability trees capable of expressing highly diverse context-specific information. The expressive power of staged trees comes at the cost of easy interpretability and the admittance of desirable properties useful in the development of causal discovery algorithms. In this talk, we consider a subfamily of staged trees, which we call CStrees, that admit an alternative representation via a sequence of DAGs. This alternate representation allows us to prove a Verma-Pearl-type characterization of model equivalence for CStrees which extends to the interventional setting, providing a graphical characterization of interventional CStree model equivalence. We will discuss these results and their potential applications to causal discovery algorithms for context-specific models based on interventional and observational data.
We consider testing and learning problems on causal Bayesian networks where the variables take values from a bounded domain. We address two problems: (i) Given access to observations and experiments on two unknown environments X and Y, test whether X=Y or X is far from Y. Here, two environments are equal if no intervention can distinguish between them. (ii) Given access to observations and experiments on an unknown environment X, learn a DAG that admits a causal model M such that X is close to M. For problem (i), we show that under natural sparsity assumptions on the underlying DAG, only O(log n) interventions and O~(n) samples/intervention is sufficient. This is joint work with Jayadev Acharya, Constantinos Daskalakis and Saravanan Kandasamy. For problem (ii), we consider the setting where there are two variables, and the goal is to learn whether X causes Y, Y causes X, or there is a hidden variable confounding the two. Under natural assumptions, we obtain a nearly tight characterization of the sample complexity that is sublinear in k. Moreover, there is a tradeoff between the number of observational samples and interventional samples. This is joint work with Jayadev Acharya, Sourbh Bhadane, Saravanan Kandasamy, and Ziteng Sun.