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We present a new approach to showing that random graphs are nearly optimal expanders. This approach is based on recent deep results in combinatorial group theory. It applies to both regular and irregular random graphs. Let G be a random…

Combinatorics · Mathematics 2015-08-24 Doron Puder

We propose a nonlinear voter model to study the emergence of global consensus in opinion dynamics. In our model, agent $i$ agrees with one of binary opinions with the probability that is a power function of the number of agents holding this…

Applications · Statistics 2011-12-30 Han-Xin Yang , Wen-Xu Wang , Ying-Cheng Lai , Bing-Hong Wang

A natural requirement of many distributed structures is fault-tolerance: after some failures, whatever remains from the structure should still be effective for whatever remains from the network. In this paper we examine spanners of general…

Data Structures and Algorithms · Computer Science 2011-02-01 Michael Dinitz , Robert Krauthgamer

In this paper we consider a dynamic Erd\H{o}s-R\'{e}nyi random graph with independent identically distributed edge processes. Our aim is to describe the joint evolution of the entries of a subgraph count vector. The main result of this…

Probability · Mathematics 2025-12-01 Rajat Subhra Hazra , Nikolai Kriukov , Michel Mandjes

In this paper, we present enumeration algorithms to list all preferred extensions of an argumentation framework. This task is equivalent to enumerating all maximal semikernels of a directed graph. For directed graphs on $n$ vertices, all…

Data Structures and Algorithms · Computer Science 2019-07-03 Serge Gaspers , Ray Li

We propose a model of network formation based on reinforcement learning, which can be seen as a generalization as the one proposed by Skyrms for signaling games. On a discrete graph, whose vertices represent individuals, at any time step…

Probability · Mathematics 2016-02-08 Daniel Kious , Pierre Tarrès

We introduce and study the reverse voter model, a dynamics for spin variables similar to the well-known voter dynamics. The difference is in the way neighbors influence each other: once a node is selected and one among its neighbors chosen,…

Statistical Mechanics · Physics 2009-11-11 Claudio Castellano

A maximal $\varepsilon$-near perfect matching is a maximal matching which covers at least $(1-\varepsilon)|V(G)|$ vertices. In this paper, we study the number of maximal near perfect matchings in generalized quasirandom and dense graphs. We…

Combinatorics · Mathematics 2019-02-07 Yifan Jing , Akbar Rafiey

In the latent voter model, which models the spread of a technology through a social network, individuals who have just changed their choice have a latent period, which is exponential with rate $\lambda$, during which they will not buy a new…

Probability · Mathematics 2016-05-31 Ran Huo , Rick Durrett

The paper considers the problem of finding the number of dominant voters in two-level voting procedures. At the first stage, voting is conducted among local groups of voters, and at the second stage, the results are aggregated to form a…

Discrete Mathematics · Computer Science 2025-06-11 N. I. Shushko , D. V. Lemtyuzhnikova

The voter model is a classical interacting particle system explaining consensus formation on a social network. Real social networks feature not only a heterogeneous degree distribution but also connections changing over time. We study the…

Probability · Mathematics 2024-09-10 John Fernley

We study a family online influence maximization problems where in a sequence of rounds $t=1,\ldots,T$, a decision maker selects one from a large number of agents with the goal of maximizing influence. Upon choosing an agent, the decision…

Machine Learning · Computer Science 2021-09-27 Gábor Lugosi , Gergely Neu , Julia Olkhovskaya

We improve on random sampling techniques for approximately solving problems that involve cuts and flows in graphs. We give a near-linear-time construction that transforms any graph on n vertices into an O(n\log n)-edge graph on the same…

Data Structures and Algorithms · Computer Science 2007-05-23 Andras Benczur , David R. Karger

Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs are given by Erd\H{o}s-R\'{e}nyi graphs $G(n,\frac{d}{n})$.…

Machine Learning · Statistics 2020-07-21 Jian Ding , Zongming Ma , Yihong Wu , Jiaming Xu

Graph vertex embeddings based on random walks have become increasingly influential in recent years, showing good performance in several tasks as they efficiently transform a graph into a more computationally digestible format while…

Machine Learning · Statistics 2021-07-22 Dominik Kloepfer , Angelica I. Aviles-Rivero , Daniel Heydecker

We study the randomized rumor spreading algorithm \emph{pull} on complete graphs with $n$ vertices. Starting with one informed vertex and proceeding in rounds, each vertex yet uninformed connects to a neighbor chosen uniformly at random and…

Probability · Mathematics 2021-10-19 Konstantinos Panagiotou , Simon Reisser

As massive graphs become more prevalent, there is a rapidly growing need for scalable algorithms that solve classical graph problems, such as maximum matching and minimum vertex cover, on large datasets. For massive inputs, several…

Data Structures and Algorithms · Computer Science 2018-12-31 Sepehr Assadi , MohammadHossein Bateni , Aaron Bernstein , Vahab Mirrokni , Cliff Stein

Given a bipartite graph, where the two sets of vertices are applicants and posts and ranks on the edges represent preferences of applicants over posts, a {\em rank-maximal} matching is one in which the maximum number of applicants is…

Data Structures and Algorithms · Computer Science 2020-09-24 Pratik Ghosal , Adam Kunysz , Katarzyna Paluch

Problems of consensus in multi-agent systems are often viewed as a series of independent, simultaneous local decisions made between a limited set of options, all aimed at reaching a global agreement. Key challenges in these protocols…

Nearest neighbor is a popular nonparametric method for classification and regression with many appealing properties. In the big data era, the sheer volume and spatial/temporal disparity of big data may prohibit centrally processing and…

Statistics Theory · Mathematics 2018-12-13 Jiexin Duan , Xingye Qiao , Guang Cheng
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