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Sparse general matrix multiplication (SpGEMM) is a fundamental building block in numerous scientific applications. One critical task of SpGEMM is to compute or predict the structure of the output matrix (i.e., the number of nonzero elements…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-07-29 Zhaoyang Du , Yijin Guan , Tianchan Guan , Dimin Niu , Nianxiong Tan , Xiaopeng Yu , Hongzhong Zheng , Jianyi Meng , Xiaolang Yan , Yuan Xie

Exclusion processes became paradigmatic models of nonequilibrium interacting particle systems of wide range applicability both across the natural and the applied, social and technological sciences. Usually they are defined as a…

Statistical Mechanics · Physics 2018-06-26 J. Ricardo G. Mendonça

We introduce a multi-species generalization of the asymmetric simple exclusion process (ASEP) with a ``no-passing" constraint, forbidding overtaking, on a one-dimensional open chain. This no-passing rule fragments the Hilbert space into an…

Statistical Mechanics · Physics 2025-04-25 Urei Miura

We propose an extension of the totally asymmetric simple exclusion process (TASEP) in which particles hopping along a lattice can be blocked by obstacles that dynamically attach/detach from lattice sites. The model can be thought as TASEP…

Statistical Mechanics · Physics 2019-02-20 Bartlomiej Waclaw , Justyna Cholewa-Waclaw , Philip Greulich

Network (or graph) sparsification compresses a graph by removing inessential edges. By reducing the data volume, it accelerates or even facilitates many downstream analyses. Still, the accuracy of many sparsification methods, with…

Social and Information Networks · Computer Science 2023-09-28 Zhen Su , Jürgen Kurths , Henning Meyerhenke

We establish a general theory for subsampling network data generated by the sparse graphon model. In contrast to previous work for networks, we demonstrate validity under minimal assumptions; the main requirement is weak convergence of the…

Statistics Theory · Mathematics 2019-08-27 Robert Lunde , Purnamrita Sarkar

The symmetric simple exclusion process (SEP) is a paradigmatic model of transport, both in and out-of-equilibrium. In this model, the study of currents and their fluctuations has attracted a lot of attention. In finite systems of arbitrary…

Statistical Mechanics · Physics 2024-11-27 Théotim Berlioz , Davide Venturelli , Aurélien Grabsch , Olivier Bénichou

We present a sequential sampling methodology for weakly structural Markov laws, arising naturally in a Bayesian structure learning context for decomposable graphical models. As a key component of our suggested approach, we show that the…

Statistics Theory · Mathematics 2019-09-04 Jimmy Olsson , Tetyana Pavlenko , Felix L. Rios

A Poisson outdegree-one graph is an oriented graph based on a Poisson point process such that each vertex has only one outgoing edge. The paper focuses on the absence of percolation for such graphs. Our main result is based on two…

Probability · Mathematics 2019-05-03 David Coupier , David Dereudre , Simon Le Stum

We derive a unified stochastic picture for the duality of a resampling-selection model with a branching-coalescing particle process (cf. http://www.ams.org/mathscinet-getitem?mr=MR2123250) and for the self-duality of Feller's branching…

Probability · Mathematics 2009-04-16 Roland Alkemper , Martin Hutzenthaler

One-dimensional asymmetric simple exclusion processes (ASEPs) which are coupled to external reservoirs via diffusive transport are studied. These ASEPs consist of active compartments characterized by directed movements of the particles and…

Statistical Mechanics · Physics 2007-05-23 Stefan Klumpp , Reinhard Lipowsky

We perform a systematic symmetry classification of the Markov generators of classical stochastic processes. Our classification scheme is based on the action of involutive symmetry transformations of a real Markov generator, extending the…

Statistical Mechanics · Physics 2025-03-13 Lucas Sá , Pedro Ribeiro , Tomaž Prosen , Denis Bernard

We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvester equation to capture both network structure and…

Machine Learning · Computer Science 2022-05-10 Shay Deutsch , Stefano Soatto

We present an exclusion process based approach for sampling densest $k$-sub-graphs from regular graphs $L$ with connected complement. By interpreting an exclusion process as a Markov chain on a corresponding Token Graph $\mathfrak{L}_k$, we…

Probability · Mathematics 2023-01-12 Jens Walter Fischer

Given a graph $G$, we form a random subgraph $G_p$ by including each edge of $G$ independently with probability $p$. We provide an asymptotic expansion of the expected number of independent sets in random subgraphs of regular bipartite…

Combinatorics · Mathematics 2026-05-14 Anna Geisler , Mihyun Kang , Michail Sarantis , Ronen Wdowinski

Pairwise Fisher graphs capture local covariance information, but they cannot distinguish an irreducible multi-observable radiation pattern from a collection of ordinary pairwise correlations. We show that this missing structure is naturally…

High Energy Physics - Phenomenology · Physics 2026-05-08 Aritra Bal , Markus Klute , Benedikt Maier , Michael Spannowsky

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a…

Methodology · Statistics 2014-10-07 Christian A. Naesseth , Fredrik Lindsten , Thomas B. Schön

For a given inhomogeneous exclusion processes on $N$ sites between two reservoirs, the trajectories probabilities allow to identify the relevant local empirical observables and to obtain the corresponding rate function at Level 2.5. In…

Statistical Mechanics · Physics 2022-01-04 Cecile Monthus

We study current fluctuations in the Totally Asymmetric Simple Exclusion Process (TASEP) on a ring with $N$ sites and $p$ particles. By introducing a deformation parameter $\gamma$, we analyze the tilted operator that governs the statistics…

Statistical Mechanics · Physics 2025-08-08 Anastasiia Trofimova , Lu Xu

Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node. While most existing approaches need to specify the neighborhood and the dependence form to…

Machine Learning · Computer Science 2018-06-06 Shupeng Gui , Xiangliang Zhang , Shuang Qiu , Mingrui Wu , Jieping Ye , Ji Liu
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