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Behzad Mehrdad

Publications and source records attributed to Behzad Mehrdad.

8 recordsLinked to original sources

An $L\log L$ theory of sparse graph convergence

Borgs, Chayes, Cohn and Zhao developed a limit theory for sparse graphs based on $L^p$ upper regularity with $p>1$, where Holder's inequality controls small sets. We develop the analogous theory in the Orlicz class $L\log L$, replacing Holder by the Young-Fenchel inequality for $Ψ(u)=(1+u)\log(1+u)-u$ and $Ψ^*(v)=e^v-1-v$. We prove a weak regularity lemma for upper $Ψ$-regular graphons with $4^{O(1/\varepsilon^2)}$ parts, using the modular $\intΨ$ as the energy; that upper $Ψ$-regular sequences have cut-metric convergent subsequences; that convergent sequences with $L\log L$ limits are upper $Ψ$-regular; and that $L\log L$ balls are cut-metric compact. The class strictly contains every $L^p$ class with $p>1$, and we give sparse $W$-random graphs that are upper $Ψ$-regular but not upper $L^p$ regular for any $p>1$. For graphs with a given degree sequence we prove, by Stein's method of exchangeable pairs with switchings, that if the average degree tends to infinity, the density to zero and the degree profile converges in $L^1$ to $φ$, uniformly random graphs converge in the normalized cut metric to $φ\otimesφ$, with no condition on the maximum degree; the sequence is upper $Ψ$-regular when $φ\in L\log L$. We also show that the maximum-entropy $β$-model is entrywise Chung-Lu when $d_{\max}^2=o(M)$, and that cut-closeness to it follows from a lower bound $e^{-o(M)}$ on its probability of having the prescribed degrees. Finally, $Ψ$ is the large deviation rate function of sparse Erdos-Renyi graphs up to a factor $1+O(1/\log\frac1p)$.

math.PR↗

Graphs versus loopless digraphs with a given degree sequence, and convergence of uniformly random graphs at every density

For a degree sequence d on n vertices with M edges, let G(d) be the number of simple graphs and D(d) the number of loopless digraphs with in-degree = out-degree = d. We conjecture that G(d) >= sqrt(D(d)) exp(-C n log(2+M/n)) for every graphical d, a single inequality across all densities, and prove it up to an extra factor log n: G(d) >= sqrt(D(d)) exp(-C n log n log(2+M/n)) for every graphical d. The proof compares pairs of graphs with Eulerian digraphs by alternately colouring closed trails; the failure of injectivity is measured exactly, shared edges cancel against 2-cycles, and parity is handled by merging trails and by a parity argument on Eulerian circuits of odd components. No asymptotic enumeration and no bound on the maximum-entropy multipliers is used. When the maximum-entropy edge probabilities lie in [delta, 1-delta], the same method gives the conjecture itself, G(d) >= sqrt(D(d)) exp(-C n log n / delta). Combined with the exact identity G(d) = exp(g*(d)) pi(d), where pi(d) is the probability that the maximum-entropy beta-model has degrees exactly d, and a van der Waerden-type bound for D(d), this shows that uniformly random graphs with degree sequence d are close in the normalized cut metric to their beta-model whenever M >> n log n log(M/n), at every density, with no tameness and no cap on the degrees. We also record the conjecture in the sparse regime and, with constant 1/2+o(1), in the dense tame regime, from the asymptotic enumerations of McKay-Wormald and Barvinok-Hartigan.

math.CO↗

On the Hawkes Process with Different Exciting Functions

The Hawkes process is a simple point process, whose intensity function depends on the entire past history and is self-exciting and has the clustering property. The Hawkes process is in general non-Markovian. The linear Hawkes process has immigration-birth representation. Based on that, Fierro et al. recently introduced a generalized linear Hawkes model with different exciting functions. In this paper, we study the convergence to equilibrium, large deviation principle, and moderate deviation principle for this generalized model. This model also has connections to the multivariate linear Hawkes process. Some applications to finance are also discussed.

math.PR↗

Limit Theorems for Empirical Density of Greatest Common Divisors

The law of large numbers for the empirical density for the pairs of uniformly distributed integers with a given greatest common divisor is a classic result in number theory. In this paper, we study the large deviations of the empirical density. We will also obtain a rate of convergence to the normal distribution for the central limit theorem. Some generalizations are provided.

math.PR↗

Moderate and Large Deviations for the Erdős-Kac Theorem

The Erdős-Kac theorem is a celebrated result in number theory which says that the number of distinct prime factors of a uniformly chosen random integer satisfies a central limit theorem. In this paper, we establish the large deviations and moderate deviations for this problem in a very general setting for a wide class of additive functions.

math.PR↗

The Speed of a Biased Walk on a Galton-Watson Tree without Leaves is Monotonic with Respect to Progeny Distributions for High Values of Bias

Consider biased random walks on two Galton-Watson trees without leaves having progeny distributions $P_1$ and $P_2$ (GW$(P_1)$ and GW$(P_2)$) where $P_1$ and $P_2$ are supported on positive integers and $P_1$ dominates $P_2$ stochastically. We prove that the speed of the walk on GW$(P_1)$ is bigger than the same on GW$(P_2)$ when the bias is larger than a threshold depending on $P_1$ and $P_2$. This partially answers a question raised by Ben Arous, Fribergh and Sidoravicius.

math.PR↗

Trees in Random Sparse Graphs with a Given Degree Sequence

Let $\mathbb{G}^{D}$ be the set of graphs $G(V,\, E)$ with $\left|V\right|=n$, and the degree sequence equal to $D=(d_{1},\, d_{2},\,\dots,\, d_{n})$. In addition, for $\frac{1}{2}<a<1$, we define the set of graphs with an almost given degree sequence $D$ as follows, \[ \mathbb{G}_{a}^{D}:=\cup\,\mathbb{G}^{\bar{D}}, \] where the union is over all degree sequences $\bar{D}$ such that, for $1\leq i\leq n$, we have $\left|d_{i}-\bar{d}_{i}\right|<d_{i}^{a}$. Now, if we chose random graphs $\mathcal{G}_{\mathbf{g}}\left(D\right)$ and $\mathcal{G}_{\mathbf{a}}\left(D\right)$ uniformly out of the sets $\mathbb{G}^{D}$ and $\mathbb{G}_{a}^{D}$, respectively, what do they look like? This has been studied when $\mathcal{G}_{\mathbf{g}}\left(D\right)$ is a dense graph, i.e. $\left|E\right|=Θ(n^{2})$, in the sense of graphons, or when $\mathcal{G}_{\mathbf{g}}\left(D\right)$ is very sparse, i.e. $d_{n}^{2}=o(\left|E\right|)$. In the case of sparse graphs with an almost given degree sequence, we investigate this question, and give the finite tree subgraph structure of $\mathcal{G}_{\mathbf{a}}\left(D\right)$ under some mild conditions. For the random graph $\mathcal{G}_{\mathbf{g}}\left(D\right)$ with a given degree sequence, we re-derive the finite tree structure in dense and very sparse cases to give a continuous picture. Moreover, for a pair of vectors $\left(D_{1},D_{2}\right)\in\mathbb{Z}^{n_{1}}\times\mathbb{Z}^{n_{2}}$, we let $\mathcal{G}_{\mathbf{b}}\left(D_{1},D_{2}\right)$ be the random bipartite graph that is chosen uniformly out of the set $\mathbb{G}^{D_{1},D_{2}}$, where $\mathbb{G}^{D_{1},D_{2}}$ is the set of all bipartite graphs with the degree sequence $\left(D_{1},D_{2}\right)$. We are able to show the result for $\mathcal{G}_{\mathbf{b}}\left(D_{1},D_{2}\right)$ without any further conditions.

math.PR↗

Two Dimensional Connectivity for Vehicular Ad-Hoc Networks

In this paper, we focus on two-dimensional connectivity in sparse vehicular ad hoc networks (VANETs). In this respect, we find thresholds for the arrival rates of vehicles at entrances of a block of streets such that the connectivity is guaranteed for any desired probability. To this end, we exploit a mobility model recently proposed for sparse VANETs, based on BCMP open queuing networks and solve the related traffic equations to find the traffic characteristics of each street and use the results to compute the exact probability of connectivity along these streets. Then, we use the results from percolation theory and the proposed fast algorithms for evaluation of bond percolation problem in a random graph corresponding to the block of the streets. We then find sufficiently accurate two dimensional connectivity-related parameters, such as the average number of intersections connected to each other and the size of the largest set of inter-connected intersections. We have also proposed lower bounds for the case of heterogeneous network with two transmission ranges. In the last part of the paper, we apply our method to several numerical examples and confirm our results by simulations.

cs.NI↗