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I present a first-principles theory of diffusion-limited aggregation in two dimensions. A renormalized mean-field approximation gives the form of the unstable manifold for branch competition, following the method of Halsey and Leibig [Phys.…

Condensed Matter · Physics 2009-10-22 Thomas C. Halsey

We consider a model of aggregation, both diffusion-limited and ballistic, based on the Cayley tree. Growth is from the leaves of the tree towards the root, leading to non-trivial screening and branch competition effects. The model exhibits…

Soft Condensed Matter · Physics 2009-10-31 M. B. Hastings , Thomas C. Halsey

We study diffusion-limited coalescence, A+A<-->A, in one dimension, in the presence of a diffusing trap. The system may be regarded as a generalization of von Smoluchowski's model for reaction rates, in that: (a) it includes reactions…

Statistical Mechanics · Physics 2015-06-25 Aleksandar Donev , Jeff Rockwell , Daniel ben-Avraham

Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. Ideally, between these two…

Machine Learning · Computer Science 2024-08-26 Samuel Cognolato , Alessandro Sperduti , Luciano Serafini

After a brief historical survey, the paper introduces the notion of entropic model sets (cut and project sets), and, more generally, the notion of diffractive point sets with entropy. Such sets may be thought of as generalizations of…

Mathematical Physics · Physics 2014-09-30 M. Baake , R. V. Moody

Diffusion models have emerged as a powerful framework for generative tasks in deep learning. They decompose generative modeling into two computational primitives: deterministic neural-network evaluation and stochastic sampling. Current…

Machine Learning · Computer Science 2026-03-31 Nihal Sanjay Singh , Mazdak Mohseni-Rajaee , Shaila Niazi , Kerem Y. Camsari

We introduce perhaps the simplest models of graph evolution with choice that demonstrate discontinuous percolation transitions and can be analyzed via mathematical evolution equations. These models are local, in the sense that at each step…

Disordered Systems and Neural Networks · Physics 2011-03-31 Raissa M. D'Souza , Michael Mitzenmacher

A number of recent studies have estimated the inter-galactic void probability function and investigated its departure from various random models. We study a family of parametric statistical models based on gamma distributions, which do give…

Mathematical Physics · Physics 2008-11-27 C. T. J. Dodson

We study a class of dynamically constructed point processes in which at every step a new point (particle) is added to the current configuration with a distribution depending on the local structure around a uniformly chosen particle. This…

Probability · Mathematics 2012-03-22 Anton Muratov , Sergei Zuyev

In galaxy clusters, hierarchical assembly predicts the formation of stellar substructures and intracluster light (ICL), a diffuse stellar component tracing the global cluster potential. Because these features are extremely faint,…

We consider a simple model of a growing cluster of points in $\Re^d,d\geq 2$. Beginning with a point $X_1$ located at the origin, we generate a random sequence of points $X_1,X_2,\ldots,X_i,\ldots,$. To generate $X_{i},i\geq 2$ we choose a…

Probability · Mathematics 2025-01-08 Alan Frieze , Ravi Kannan , Wesley Pegden

Spectral clustering is discussed from many perspectives, by extending it to rectangular arrays and discrepancy minimization too. Near optimal clusters are obtained with singular value decomposition and with the weighted $k$-means algorithm.…

Combinatorics · Mathematics 2022-01-06 Marianna Bolla , Vilas Winstein , Tao You , Frank Seidl , Fatma Abdelkhalek

This paper studies clustering algorithms for dynamically evolving graphs $\{G_t\}$, in which new edges (and potential new vertices) are added into a graph, and the underlying cluster structure of the graph can gradually change. The paper…

Data Structures and Algorithms · Computer Science 2024-06-06 Steinar Laenen , He Sun

We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between…

Machine Learning · Statistics 2015-03-19 John Paisley , Chong Wang , David Blei

In this article we define and study a stochastic process on Galoisian covers of compact manifolds. The successive positions of the process are defined recursively by picking a point uniformly in the Dirichlet domain of the previous one. We…

Probability · Mathematics 2022-02-18 Adrien Boulanger , Olivier Glorieux

In this paper we report the results of simulations of a 2D gravity driven, dissipative granular flow through a hopper system. Measurements of impulse distributions P(I) on the simulated system show flow-velocity-invariant behavior of the…

Soft Condensed Matter · Physics 2009-11-10 A. Ferguson , B. Fisher , B. Chakraborty

Extensive numerical simulation are reported for the structure and dynamics of large clusters on metal(100) surfaces. Different types of perimeter hopping processes makes center-of-mass of the cluster to follow a a random walk trajectory.…

Statistical Mechanics · Physics 2007-05-23 J. R. Sanchez

Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design. Diffusion generative models, as an emerging research focus, have been applied to graph generation tasks.…

Machine Learning · Computer Science 2024-11-05 Zhe Xu , Ruizhong Qiu , Yuzhong Chen , Huiyuan Chen , Xiran Fan , Menghai Pan , Zhichen Zeng , Mahashweta Das , Hanghang Tong

In this paper we propose algorithms for allocating $n$ sequential balls into $n$ bins that are interconnected as a $d$-regular $n$-vertex graph $G$, where $d\ge3$ can be any integer.Let $l$ be a given positive integer. In each round $t$,…

Data Structures and Algorithms · Computer Science 2016-03-01 Ali Pourmiri

We consider long-range percolation on $\mathbb{Z}^d$, where the probability that two vertices at distance $r$ are connected by an edge is given by $p(r)=1-\exp[-\lambda(r)]\in(0,1)$ and the presence or absence of different edges are…

Probability · Mathematics 2011-01-10 Pieter Trapman
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