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Partha S. Dey

Publications and source records attributed to Partha S. Dey.

At least 19 recordsLinked to original sources

Inhomogeneous Long-Range First-Passage Percolation in a Random Vertex Environment

We study inhomogeneous long-range first-passage percolation on $\mathbb{Z}^d$ with edge passage times $\lVert x-y\rVert^αω_{xy}/(V_xV_y)$, where vertex weights have polynomial upper-tail exponent $γ$ and edge noises have polynomial lower-tail exponent $θ$ at zero. The model interpolates between long-range first-passage percolation and scale-free percolation and exhibits competition between reusable heavy-vertex hubs and pair-specific small-noise bridges. We conjecture an eight-regime phase diagram, organized into five growth phases governed by $q_{\rm hub}=d/γ$ and $q_{\rm edge}=d/θ$. For $T_n=T(0,\lceil nx\rceil)$, we prove upper bounds of the conjectured order in every regime and matching lower bounds in phases I and II. Specifically, $T_n=0$ a.s. when $α<q_{\rm hub}\vee q_{\rm edge}$, while $T_n=Θ_{\mathbb{P}}(1)$ when $q_{\rm hub}\vee q_{\rm edge}<α<2q_{\rm hub}$. In the edge-dominated intermediate regime, $T_n=O_{\mathbb{P}}((\log n)^{Δ_{\rm III}+\varepsilon})$, where $Δ_{\rm III}=\log 2/\log(2q_{\rm edge}/α)$. The two power-law regimes satisfy $T_n=O_{\mathbb{P}}(n^{α-2q_{\rm hub}+\varepsilon})$ and $T_n=O_{\mathbb{P}}(n^{α-2q_{\rm edge}+\varepsilon})$, and the linear regime satisfies $T_n=O_{\mathbb{P}}(n)$. All upper bounds are constructive, based on hub-chain and binary edge-bridge multiscale constructions.

math.PR

First-Passage Percolation on Spread-out line graphs: Microscopic Regime

We study first-passage percolation on the $\ell$-spread-out line graph, where each vertex $i\in\{0,1,\dots,n\}$ is connected to all others at distance at most $\ell$. Here, we focus on the microscopic regime, with $\ell$ fixed as $n\to\infty$. Independent nonnegative weights are assigned to these edges. We obtain a law of large numbers and precise fluctuation results for the passage time $T_n$ from $0$ to $n$. If the weight distribution has finite variance or a heavy tail with exponent above $2/\ell_c$ where $\ell_c=\ell(\ell+1)/2$, then $T_n$ satisfies a Gaussian CLT with $\sqrt{n}$ scaling. In contrast, for heavier-tailed distributions, with index below the threshold, we show that $T_n$, appropriately centered and scaled, converges to a non-Gaussian stable law. We also prove an LLN and CLT for the number of edges in the minimizing path. The key tool is a pivot-node decomposition; the geodesic can be segmented into i.i.d. blocks, leading to a renewal structure. Our results extend the classical one-dimensional CLT to include finite-range connectivity and heavy tails, revealing a new distributional phase transition in the fluctuations of $T_n$.

math.PR

Phase Transition and Fluctuation Results for First-Passage Percolation on Spread-Out Cycle Graphs

We study first-passage percolation on the $\ell$-spread-out one-dimensional cycle of size $n$, where vertices are connected if their graph distance is at most $\ell$. We assign i.i.d.~non-negative random weights from a Weibull distribution $ω_e \sim \mathrm{Exp}(1)^{1/θ}$ to the edges for $θ>0$ fixed. This paper investigates the transition in the asymptotic behavior of the passage time $T_n$ between two typical vertices and the hop-count of the optimal path as the connectivity parameter $\ell$ diverges with $n$. We identify two fundamentally distinct geometric regimes. In the mesoscopic regime ($1 \ll \ell \ll n$), the optimal path locally mimics a spatial branching random walk but remains globally constrained to a one-dimensional geometry. We establish a law of large numbers characterized by the front speed of a Crump--Mode--Jagers branching random walk, prove a central limit theorem with Gaussian fluctuations when $\ell\ll n^{1/4}$, and show that the expected hop-count grows proportionally with the spatial distance. In the macroscopic regime ($\ell \approx λn$ for $λ\in (0,1/2)$), the graph becomes a highly connected mean-field network. We prove that the passage time collapses to a $\log n$ scale with constant order non-Gaussian fluctuations, explicitly determining the extreme-value limit driven by the collision of two independent non-spatial CMJ processes. We establish a law of large numbers for the hop-count. Finally, we rigorously trace the transition in the order of the mean of $T_n$ between these two regimes, demonstrating an order transition for the passage time across the critical connectivity threshold $\ell \asymp n/\log n$. Our results provide a comprehensive deterministic-range interpolation from spatial Gaussian fluctuations to mean-field extreme-value fluctuations.

math.PR

Fluctuations of the free energy of the Sherrington-Kirkpatrick model with ferromagnetic interaction

We study the Sherrington-Kirkpatrick model with an additional Curie-Weiss ferromagnetic interaction (SKFI), whose phase diagram in the plane of the disorder strength $β$ and the ferromagnetic coupling $γ$ consists of a paramagnetic, a ferromagnetic, and a spin glass region. Our main result is a central limit theorem for the free energy in the ferromagnetic regime: at scale $N^{-1/2}$, the fluctuations coincide with those of the Sherrington-Kirkpatrick model in an effective external field determined by the limiting magnetization. We prove this for general mixed even $p$-spin interactions, using the fluctuation theory of Chen, Dey, and Panchenko. For the pure 2-spin model we complete the picture of the phase diagram: we give a new proof of the order-$N^{-1}$ Gaussian fluctuations in the paramagnetic regime, obtained earlier by Banerjee, valid up to the critical window; we determine the limiting distribution on the critical line $γ= 1 + θN^{-1/2}$ separating the paramagnetic and ferromagnetic regimes; and we show that in the spin glass regime the fluctuations coincide with those of the zero-field SK model under a widely believed variance-divergence assumption. Our results form the Ising analogue of the fluctuation results of Baik and Lee for the spherical SKFI model.

math.PR

Random optimization problems at fixed temperatures

This article considers a class of disordered mean-field combinatorial optimization problems. We focus on the Gibbs measure, where the inverse temperature does not vary with the size of the graph and the edge weights are sampled from a general distribution under mild assumptions. Our results consist of the Law of Large Numbers and Central Limit Theorems for the log-partition function, the weight of a typical configuration, and the Gibbs average in both quenched and annealed forms. We also derive quenched Poisson convergence for the size of the intersection of two independent samples, yielding replica symmetry of the model. Applications cover popular models from the literature, such as the Minimal Matching Problem, Traveling Salesman Problem, and Minimal Spanning Tree Problem, on a sequence of deterministic and random dense graphs of increasing size.

math.PR

Limiting distributions of triangle counts in linear preferential attachment models

We derive distributional approximations for the number of triangles in the linear preferential attachment model $\mathrm{PAM}(m,δ)$, where $m\ge 2$ and $δ>-m$, with explicit rates of convergence. The limiting distribution undergoes a phase transition from Gaussian to another nontrivial distribution, which we characterize explicitly. The asymptotic behavior is governed by the interplay between the hidden random environment and the mean-field interaction effect. In particular, our analysis also yields a continuous phase transition in the expected number of triangles as $δ$ varies.

math.PR

Scaling Limit of a Stochastic Clustering Model on $\mathbb{R}$

We consider an infinite-dimensional stochastic clustering model on $\mathbb{R}$. In discrete time, each point of a unit-intensity simple point process moves halfway toward either of its left or right neighbors, chosen uniformly at random. Co-located points are merged into a single point, and the resulting simple point process is rescaled to unit intensity. We show that, when the point processes are shifted so that there is a point at the origin, the dynamics have a unique weak limit when the initial point process is renewal. For this limiting point process, the gap distribution has exponential tails. We also show that for the time-reversed process and with an appropriate scaling in space, there is a limiting (random) distribution function on $\mathbb{R}$, whose associated measure assigns to $\mathbb{R}$ a measure corresponding to the gap between consecutive points. Finally, we discuss several relevant research directions.

math.PR

Fluctuations for the Sherrington--Kirkpatrick spin glass model near the critical temperature

We consider the Sherrington--Kirkpatrick spin glass model with zero external field and at inverse temperature $β>0$. Let $F_N(β)$ be the corresponding log-partition function. Under the assumption that $c_N:=N^{1/3}(1-β_N^2)$ is bounded away from $0$, we prove that Var$(F_N(β_N)) = - \frac{1}{2} \log (1-β_N^2) -{β_N^2}/{2} + O( c_N^{-3/2}).$ As a consequence, we obtain Var$(F_N(1-c N^{-1/3})) = \frac16\log N + O(1)$ for any fixed constant $c\in(0,\infty)$. We also prove a Gaussian central limit theorem for the centered and scaled $F_N(β_N)$.

math.PR

The Time to Consensus in a Blockchain: Insights into Bitcoin's "6 Blocks Rule''

We investigate the time to consensus in Nakamoto blockchains. Specifically, we consider two competing growth processes, labeled \emph{honest} and \emph{adversarial}, and determine the time after which the honest process permananetly exceeds the adversarial process. This is done via queueing techniques. The predominant difficulty is that the honest growth process is subject to \emph{random delays}. In a stylized Bitcoin model, we compute the Laplace transform for the time to consensus and verify it via simulation.

cs.DC

A Framework for Blockchain Architecture Design

Emerging applications of blockchains, such as grocery supply chains, require frequent updates to the data structure. This is in contrast with typical analyses of the Bitcoin blockchain, in which updates occur infrequently. With more frequent updates, the spread of blocks among participants in the blockchain protocol becomes complicated; thus, the structure of the blockchain data structure itself can differ significantly from the structure without the presence of network delays. In addition, emerging blockchain applications such as internet-of-things or supply chain warrant different architectures of the blockchain data structure, and so one needs a general understanding of how the data structure works rather than focusing on the specific architecture of Bitcoin. In this paper, we develop a new model to study the dynamics of the blockchain data structure in the presence of i.i.d.~network delays. Specifically, we consider an asymptotic design criterion called one-endedness, which should be satisfied by all blockchain architectures. We develop techniques to show that the one-endedness property holds for some of the leading blockchain architectures.

math.PR

The 2R-Conjecture for the Hegselmann--Krause Model: A Proof in Expectation and New Directions

Hegselmann--Krause models are localized, distributed averaging dynamics on spatial data. A key aspect of these dynamics is that they lead to cluster formation, which has important applications in geographic information systems, dynamic clustering algorithms, opinion dynamics, and social networks. For these models, the key questions are whether a fixed point exists and, if so, characterizing it. In this work, we establish new results towards the "2R-Conjecture" for the Hegselmann--Krause model, for which no meaningful progress, or even any precise statement, has been made since its introduction in 2007. This conjecture relates to the structure of the fixed point when there are a large number of agents per unit space. We provide, among other results, a proof in expectation and a statement of a stronger result that is supported by simulation. The key methodological contribution is to consider the dynamics as an infinite-dimensional problem on the space of point processes, rather than on finitely many points. This enables us to leverage stationarity, shift invariance, and certain other symmetries to obtain the results. These techniques do not have finite-dimensional analogs.

physics.soc-ph

Berry-Esseen Theorem for Sample Quantiles with Locally Dependent Data

We derive a Gaussian Central Limit Theorem for the sample quantiles based on locally dependent random variables with explicit convergence rate. Our approach is based on converting the problem to a sum of indicator random variables, applying Stein's method for local dependence, and bounding the distance between two normal distributions. We also generalize this approach to the joint convergence of sample quantiles with an explicit convergence rate.

math.PR

Curie-Weiss Model under $\ell^{p}$ constraint and a Generalized Hubbard-Stratonovich Transform

We consider the Ising Curie-Weiss model on the complete graph constrained under a given $\ell^{p}$ norm for some $p>0$. For $p=\infty$, it reduces to the classical Ising Curie-Weiss model. We prove that for all $p>2$, there exists $β_{c}(p)$ such that for $β<β_{c}(p)$, the magnetization is concentrated at zero and satisfies an appropriate Gaussian CLT. In contrast, for $β>β_{c}(p)$ the magnetization is concentrated at $\pm m_\ast$ for some $m_\ast>0$. We have $β_{c}(p)>1$ for $p>2$ and $\lim_{p\to\infty}β_{c}(p)=3$. We further generalize the model for general symmetric spin distributions and prove a similar phase transition. For $0<p<1$, the log-partition function scales at the order of $n^{2/p-1}$. The proofs are based on a generalized Hubbard-Stratonovich (GHS) transform, which is of independent interest.

math.PR

Hypergraph Counting and Mixed $p$-Spin Glass Models under Replica Symmetry

We study the fluctuation problems at high temperature in the general mixed $p$-spin glass models under the weak external field assumption: $h= ρN^{-α}, ρ>0, α\in [1/4,\infty]$. By extending the cluster expansion approach to this generic setting, we convert the fluctuation problem as a hypergraph counting problem and thus obtain a new multiple-transition phenomenon. A by-product of our results is a new critical inverse temperature obtained from optimal second moment estimates. In particular, all our fluctuation results hold up to the threshold. Combining with multivariate Stein's method, we also obtain an explicit convergence rate under proper moment assumptions on the general symmetric disorder. Our results have several further implications. First, our approach works for both even and odd pure $p$-spin models. The leading cluster structures in the odd $p$ case are different and more involved than in the even $p$ case. This combinatorially explains the folklore that odd $p$-spin is more complicated than even $p$. Second, in the mixed $p$-spin setting, the cluster structures differ depending on the relation between the minimum effective even and odd $p$-spins: $p_e$ and $p_o$. As an example, at $h=0$, there are three sub-regimes: $p_e<p_o, p_o<p_e<2p_o, p_e\ge 2p_o$, wherein the first and third ones, the mixed model behaves essentially like a pure $p$-spin model, and only in the second regime, it is more like a mixture. This gives another criterion for classifying mean-field spin glass models compared to the work of Auffinger and Ben Arous (Ann.~Probab.~41 (2013), no.~6, 4214--4247), where the idea is based on complexity computations for spherical models. Third, our framework naturally implies a multi-scale fluctuation phenomenon conjectured in the work of Bovier and Schertzer (Probab. Theory Relat. Fields (2024)).

math.PR

Central Limit Theorem for Gram-Schmidt Random Walk Design

We prove a central limit theorem for the Horvitz-Thompson estimator based on the Gram-Schmidt Walk (GSW) design, recently developed in Harshaw et al.(2022). In particular, we consider the version of the GSW design which uses randomized pivot order, thereby answering an open question raised in the same article. We deduce this under minimal and global assumptions involving only the problem parameters such as the (sum) potential outcome vector and the covariate matrix. As an interesting consequence of our analysis we also obtain the precise limiting variance of the estimator in terms of these parameters which is smaller than the previously known upper bound. The main ingredients are a simplified skeletal process approximating the GSW design and concentration phenomena for random matrices obtained from random sampling using the Stein's method for exchangeable pairs.

math.ST

Collaboration of Random Walks on Graphs

Consider a collaborative dynamic of $k$ independent random walks on a finite connected graph $G$. We are interested in the size of the set of vertices visited by at least one walker and study how the number of walkers relates to the efficiency of covering the graph. To this end, we show that the expected size of the union of ranges of $k$ independent random walks with lifespans $t_1,t_2,\ldots,t_k$, respectively, is greater than or equal to that of a single random walk with the lifespan equal to $t_1+t_2+\cdots+t_k$. We analyze other related graph exploration schemes and end with many open questions.

math.PR

Phase Transition for Discrete Non Linear Schrödinger Equation in Three and Higher Dimensions

We analyze the thermodynamics of the focusing discrete nonlinear Schrödinger equation in dimensions $d\ge 3$ with general nonlinearity $p>1$ and under a model with two parameters, representing inverse temperature and strength of the nonlinearity, respectively. We prove the existence of limiting free energy and analyze the phase diagram for general $d,p$. We also prove the existence of a continuous phase transition curve that divides the parametric plane into two regions involving the appearance or non-appearance of solitons. Appropriate upper and lower bounds for the curve are constructed. We also look at the typical behavior of a function chosen from the Gibbs measure for certain parts of the phase diagram.

math.AP

Stein's method for Conditional Central Limit Theorem

In the seventies, Charles Stein revolutionized the way of proving the Central Limit Theorem by introducing a method that utilizes a characterization equation for Gaussian distribution. In the last 50 years, much research has been done to adapt and strengthen this method to a variety of different settings and other limiting distributions. However, it has not been yet extended to study conditional convergences. In this article, we develop a novel approach using Stein's method for exchangeable pairs to find a rate of convergence in Conditional Central Limit Theorem of the form $(X_n\mid Y_n=k)$, where $(X_n, Y_n)$ are asymptotically jointly Gaussian, and extend this result to a multivariate version. We apply our general result to several concrete examples, including pattern count in a random binary sequence and subgraph count in Erdös-Rényi random graph.

math.PR