Random Regular Graphs Research Articles

Belief propagation on replica symmetric random factor graph models

According to physics predictions, the free energy of random factor graph models that satisfy a certain “static replica symmetry” condition can be calculated via the Belief Propagation message passing scheme [Krzakala et al., PNAS 2007]. Here we prove this conjecture for two general classes of random factor graph models, namely Poisson random factor graphs and random regular factor graphs. Specifically, we show that the messages constructed just as in the case of acyclic factor graphs asymptotically satisfy the Belief Propagation equations and that the free energy density is given by the Bethe free energy formula.

Local Algorithms, Regular Graphs of Large Girth, and Random Regular Graphs

We introduce a general class of algorithms and analyse their application to regular graphs of large girth. In particular, we can transfer several results proved for random regular graphs into (deterministic) results about all regular graphs with sufficiently large girth. This reverses the usual direction, which is from the deterministic setting to the random one. In particular, this approach enables, for the first time, the achievement of results equivalent to those obtained on random regular graphs by a powerful class of algorithms which contain prioritised actions. As a result, we obtain new upper or lower bounds on the size of maximum independent sets, minimum dominating sets, maximum k-independent sets, minimum k-dominating sets and maximum k-separated matchings in r-regular graphs with large girth.

Critical percolation on random regular graphs

First published in Proc. Amer. Math. Soc. 146 (2018), 3321-3332, published by the American Mathematical Society

Spectra of overlapping Wishart matrices and the Gaussian free field

Consider a doubly-infinite array of i.i.d. centered variables with moment conditions, from which one can extract a finite number of rectangular, overlapping submatrices, and form the corresponding Wishart matrices. We show that under basic smoothness assumptions, centered linear eigenstatistics of such matrices converge jointly to a Gaussian vector with an interesting covariance structure. This structure, which is similar to those appearing in [A. Borodin, Clt for spectra of submatrices of Wigner random matrices, Mosc. Math. J. 14(1) (2014) 29–38; A. Borodin and V. Gorin, General beta Jacobi corners process and the Gaussian free field, preprint (2013), arXiv:1305.3627; T. Johnson and S. Pal, Cycles and eigenvalues of sequentially growing random regular graphs, Ann. Probab. 42(4) (2014) 1396–1437], can be described in terms of the height function, and leads to a connection with the Gaussian Free Field on the upper half-plane. Finally, we generalize our results from univariate polynomials to a special class of planar functions.

Sudden spreading of infections in an epidemic model with a finite seed fraction

We study a simple case of the susceptible-weakened-infected-removed model in regular random graphs in a situation where an epidemic starts from a finite fraction of initially infected nodes (seeds). Previous studies have shown that, assuming a single seed, this model exhibits a kind of discontinuous transition at a certain value of infection rate. Performing Monte Carlo simulations and evaluating approximate master equations, we find that the present model has two critical infection rates for the case with a finite seed fraction. At the first critical rate the system shows a percolation transition of clusters composed of removed nodes, and at the second critical rate, which is larger than the first one, a giant cluster suddenly grows and the order parameter jumps even though it has been already rising. Numerical evaluation of the master equations shows that such sudden epidemic spreading does occur if the degree of the underlying network is large and the seed fraction is small.

Replica Bounds by Combinatorial Interpolation for Diluted Spin Systems

In two papers Franz, Leone and Toninelli proved bounds for the free energy of diluted random constraints satisfaction problems, for a Poisson degree distribution [5] and a general distribution [6]. Panchenko and Talagrand [16] simplified the proof and generalized the result of [5] for the Poisson case. We provide a new proof for the general degree distribution case and as a corollary, we obtain new bounds for the size of the largest independent set (also known as hard core model) in a large random regular graph. Our proof uses a combinatorial interpolation based on biased random walks [21] and allows to bypass the arguments in [6] based on the study of the Sherrington-Kirkpatrick (SK) model.

Improved belief propagation algorithm finds many Bethe states in the random-field Ising model on random graphs.

We first present an empirical study of the Belief Propagation (BP) algorithm, when run on the random field Ising model defined on random regular graphs in the zero temperature limit. We introduce the notion of extremal solutions for the BP equations, and we use them to fix a fraction of spins in their ground state configuration. At the phase transition point the fraction of unconstrained spins percolates and their number diverges with the system size. This in turn makes the associated optimization problem highly non trivial in the critical region. Using the bounds on the BP messages provided by the extremal solutions we design a new and very easy to implement BP scheme which is able to output a large number of stable fixed points. On one hand this new algorithm is able to provide the minimum energy configuration with high probability in a competitive time. On the other hand we found that the number of fixed points of the BP algorithm grows with the system size in the critical region. This unexpected feature poses new relevant questions about the physics of this class of models.

Comparison of Gabay–Toulouse and de Almeida–Thouless instabilities for the spin-glass XY model in a field on sparse random graphs

Vector spin glasses are known to show two different kinds of phase transitions in presence of an external field: the so-called de Almeida-Thouless and Gabay-Toulouse lines. While the former has been studied to some extent on several topologies (fully connected, random graphs, finite-dimensional lattices, chains with long-range interactions), the latter has been studied only in fully connected models, which however are known to show some unphysical behaviors (e.g. the divergence of these critical lines in the zero-temperature limit). Here we compute analytically both these critical lines for XY spin glasses on random regular graphs. We discuss the different nature of these phase transitions and the dependence of the critical behavior on the field distribution. We also study the crossover between the two different critical behaviors, by suitably tuning the field distribution.

Random Regular Graph and Generalized De Bruijn Graph with $k$ -Shortest Path Routing

The Random regular graph (RRG) has recently been proposed as an interconnect topology for future large scale data centers and HPC clusters. An RRG is a special case of directed regular graph (DRG) where each link is unidirectional and all nodes have the same number of incoming and outgoing links. In this work, we establish bounds for DRGs on diameter, average $k$ -shortest path length, and a load balancing property with $k$ -shortest path routing, and use these bounds to evaluate RRGs. The results indicate that an RRG with $k$ -shortest path routing is not ideal in terms of diameter and load balancing. We further consider the Generalized De Bruijn Graph (GDBG), a deterministic DRG, and prove that for most network configurations, a GDBG is near optimal in terms of diameter, average $k$ -shortest path length, and load balancing with a $k$ -shortest path routing scheme. Finally, we use modeling and simulation to exploit the strengths and weaknesses of RRGs for different traffic conditions by comparing RRGs with GDBGs.

Convergence of maximum bisection ratio of sparse random graphs

We consider sequences of large sparse random graphs whose degree distribution approaches a limit with finite mean. This model includes both the random regular graphs and the Erd\"os-Renyi graphs of constant average degree. We prove that the maximum bisection ratio of such a graph sequence converges almost surely to a deterministic limit. We extend this result to so-called 2-spin spin glasses in the paramagnetic to ferromagnetic regime. Our work generalizes the graph interpolation method to some non-additive graph parameters.

Size biased couplings and the spectral gap for random regular graphs

Let $\lambda$ be the second largest eigenvalue in absolute value of a uniform random $d$-regular graph on $n$ vertices. It was famously conjectured by Alon and proved by Friedman that if $d$ is fixed independent of $n$, then $\lambda=2\sqrt{d-1} +o(1)$ with high probability. In the present work we show that $\lambda=O(\sqrt{d})$ continues to hold with high probability as long as $d=O(n^{2/3})$, making progress towards a conjecture of Vu that the bound holds for all $1\le d\le n/2$. Prior to this work the best result was obtained by Broder, Frieze, Suen and Upfal (1999) using the configuration model, which hits a barrier at $d=o(n^{1/2})$. We are able to go beyond this barrier by proving concentration of measure results directly for the uniform distribution on $d$-regular graphs. These come as consequences of advances we make in the theory of concentration by size biased couplings. Specifically, we obtain Bennett-type tail estimates for random variables admitting certain unbounded size biased couplings.

Non-ergodic delocalized phase in Anderson model on Bethe lattice and regular graph

We develop a novel analytical approach to the problem of single particle localization in infinite dimensional spaces such as Bethe lattice and random regular graph models. The key ingredient of the approach is the notion of the inverted order thermodynamic limit (IOTL) in which the coupling to the environment goes to zero before the system size goes to infinity. Using IOTL and Replica Symmetry Breaking (RSB) formalism we derive analytical expressions for the fractal dimension D1 that distinguishes between the extended ergodic, D1=1, and extended non-ergodic (multifractal), 0<D1<1 states on the Bethe lattice and random regular graphs with the branching number K. We also employ RSB formalism to derive the analytical expression lnStyp−1=−〈lnS〉∼(Wc−W)−1 for the typical imaginary part of self-energy Styp in the non-ergodic phase close to the Anderson transition in the conventional thermodynamic limit. We prove the existence of an extended non-ergodic phase in a broad range of disorder strength and energy and establish the phase diagrams of the models as a function of disorder and energy. The results of the analytical theory are compared with large-scale population dynamics and with the exact diagonalization of Anderson model on random regular graphs. We discuss the consequences of these results for the many body localization.

Asymmetric learning ability promotes cooperation in structured populations

In this paper, we consider an asymmetric learning ability in structured population. The rate of strategy adoption from one randomly neighbor is controlled by the payoff difference and a factor α. Players of type B will think over α (α > 1) times when he decides whether to adopt his opponent's strategy. While players of type A just consider only once. The concentration of players A and B are denoted by v and 1−v and remains unchanged during the simulations. Through numerous computing simulations, we find that our new setup of asymmetric learning ability can promote the evolution cooperation in the spatial prisoner's dilemma game. Furthermore, in order to explore the generality of this finding, we have tested the results on spatial public good games and random regular graphs.

Finite plateau in spectral gap of polychromatic constrained random networks.

We consider critical behavior in the ensemble of polychromatic Erdős-Rényi networks and regular random graphs, where network vertices are painted in different colors. The links can be randomly removed and added to the network subject to the condition of the vertex degree conservation. In these constrained graphs we run the Metropolis procedure, which favors the connected unicolor triads of nodes. Changing the chemical potential, μ, of such triads, for some wide region of μ, we find the formation of a finite plateau in the number of intercolor links, which exactly matches the finite plateau in the network algebraic connectivity (the value of the first nonvanishing eigenvalue of the Laplacian matrix, λ_{2}). We claim that at the plateau the spontaneously broken Z_{2} symmetry is restored by the mechanism of modes collectivization in clusters of different colors. The phenomena of a finite plateau formation holds also for polychromatic networks with M≥2 colors. The behavior of polychromatic networks is analyzed via the spectral properties of their adjacency and Laplacian matrices.

Distribution of shortest cycle lengths in random networks.

We present analytical results for the distribution of shortest cycle lengths (DSCL) in random networks. The approach is based on the relation between the DSCL and the distribution of shortest path lengths (DSPL). We apply this approach to configuration model networks, for which analytical results for the DSPL were obtained before. We first calculate the fraction of nodes in the network which reside on at least one cycle. Conditioning on being on a cycle, we provide the DSCL over ensembles of configuration model networks with degree distributions which follow a Poisson distribution (Erdős-Rényi network), degenerate distribution (random regular graph), and a power-law distribution (scale-free network). The mean and variance of the DSCL are calculated. The analytical results are found to be in very good agreement with the results of computer simulations.

Universality of cutoff for the Ising model

On any locally-finite geometry, the stochastic Ising model is known to be contractive when the inverse-temperature $\beta$ is small enough, via classical results of Dobrushin and of Holley in the 1970s. By a general principle proposed by Peres, the dynamics is then expected to exhibit cutoff. However, so far cutoff for the Ising model has been confirmed mainly for lattices, heavily relying on amenability and log Sobolev inequalities. Without these, cutoff was unknown at any fixed $\beta>0$, no matter how small, even in basic examples such as the Ising model on a binary tree or a random regular graph. We use the new framework of information percolation to show that, in any geometry, there is cutoff for the Ising model at high enough temperatures. Precisely, on any sequence of graphs with maximum degree $d$, the Ising model has cutoff provided that $\beta<\kappa/d$ for some absolute constant $\kappa$ (a result which, up to the value of $\kappa$, is best possible). Moreover, the cutoff location is established as the time at which the sum of squared magnetizations drops to 1, and the cutoff window is $O(1)$, just as when $\beta=0$. Finally, the mixing time from almost every initial state is not more than a factor of $1+\varepsilon_{\beta}$ faster then the worst one (with $\varepsilon_{\beta}\to0$ as $\beta\to0$), whereas the uniform starting state is at least $2-\varepsilon_{\beta}$ times faster.

Bulk eigenvalue statistics for random regular graphs

AK was partly supported by Swiss National Science Foundation grant 144662. HTY was partly supported by NSF grant DMS-1307444 and a Simons Investigator fellowship.

Effects of fundamentals acquisition and strategy switch on stock price dynamics

An agent-based artificial stock market is developed to simulate trading behavior of investors. In the market, acquisition and employment of information about fundamentals and strategy switch are investigated to explain stock price dynamics. Investors could obtain the information from both market and neighbors resided on their social networks. Depending on information status and performances of different strategies, an informed investor may switch to the strategy of fundamentalist. This in turn affects the information acquisition process, since fundamentalists are more inclined to search and spread the information than chartists. Further investigation into price dynamics generated from three typical networks, i.e. regular lattice, small-world network and random graph, are conducted after general relation between network structures and price dynamics is revealed. In each network, integrated effects of different combinations of information efficiency and switch intensity are investigated. Results have shown that, along with increasing switch intensity, market and social information efficiency play different roles in the formation of price distortion, standard deviation and kurtosis of returns.

Minimal dominating set problem studied by simulated annealing and cavity method: analytics and population dynamics

The minimal dominating set (MDS) problem is a prototypical hard combinatorial optimization problem. We recently studied this problem using the cavity method. Although we obtained a solution for a given graph that gives a very good estimation of the minimal dominating size, we do not know whether there is a ground state solution or how many solutions exist in the ground state. We have therefore continued to develop a one-step replica symmetry breaking theory to investigate the ground state energy of the MDS problem. First, we find that the solution space for the MDS problem exhibits both condensation transition and cluster transition on regular random graphs, and prove this using a simulated annealing dynamical process. Second, we develop a zero-temperature survey propagation algorithm on Erdős–Rényi random graphs to estimate the ground state energy, and obtain a survey propagation decimation algorithm that achieves results as good as the belief propagation decimation algorithm.

Topological network entanglement as order parameter for the emergence of geometry

We show that, in discrete models of quantum gravity, emergent geometric space can be viewed as the entanglement pattern in a mixed quantum state of the ‘universe’, characterized by a universal topological network entanglement. As a concrete example we analyze the recently proposed model in which geometry emerges due to the condensation of 4-cycles in random regular bipartite graphs, driven by the combinatorial Ollivier–Ricci curvature. Using this model we show that the emergence of geometric order decreases the entanglement entropy of random configurations. The lowest geometric entanglement entropy is realized in four dimensions.