no code implementations • ICML 2020 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

Graph matching, also known as network alignment, aims at recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs.

no code implementations • 1 Feb 2024 • Cheng Mao, Alexander S. Wein, Shenduo Zhang

We study a random graph model for small-world networks which are ubiquitous in social and biological sciences.

no code implementations • 17 Apr 2023 • Abhishek Dhawan, Cheng Mao, Alexander S. Wein

We consider detecting the presence of a planted $G^r(n^\gamma, n^{-\alpha})$ subhypergraph in a $G^r(n, n^{-\beta})$ hypergraph, where $0< \alpha < \beta < r-1$ and $0 < \gamma < 1$.

no code implementations • 20 Feb 2023 • Abhishek Dhawan, Cheng Mao, Ashwin Pananjady

We consider a symmetric mixture of linear regressions with random samples from the pairwise comparison design, which can be seen as a noisy version of a type of Euclidean distance geometry problem.

no code implementations • 13 Feb 2023 • Cheng Mao, Alexander S. Wein, Shenduo Zhang

Planted dense cycles are a type of latent structure that appears in many applications, such as small-world networks in social sciences and sequence assembly in computational biology.

no code implementations • 25 Sep 2022 • Cheng Mao, Yihong Wu, Jiaming Xu, Sophie H. Yu

We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex.

no code implementations • 22 Oct 2021 • Cheng Mao, Yihong Wu, Jiaming Xu, Sophie H. Yu

We propose a new procedure for testing whether two networks are edge-correlated through some latent vertex correspondence.

no code implementations • 11 Oct 2021 • Cheng Mao, Mark Rudelson, Konstantin Tikhomirov

Let $G$ and $G'$ be $G(n, p)$ Erd\H{o}s--R\'enyi graphs marginally, identified with their adjacency matrices.

no code implementations • 31 May 2021 • Cheng Mao, Alexander S. Wein

Recovering a planted vector $v$ in an $n$-dimensional random subspace of $\mathbb{R}^N$ is a generic task related to many problems in machine learning and statistics, such as dictionary learning, subspace recovery, principal component analysis, and non-Gaussian component analysis.

no code implementations • 28 Jan 2021 • Cheng Mao, Mark Rudelson, Konstantin Tikhomirov

Graph matching, also known as network alignment, refers to finding a bijection between the vertex sets of two given graphs so as to maximally align their edges.

no code implementations • 14 Sep 2020 • Cheng Mao, Yihong Wu

In applications such as rank aggregation, mixture models for permutations are frequently used when the population exhibits heterogeneity.

no code implementations • 20 Jul 2019 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

We analyze a new spectral graph matching algorithm, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs.

no code implementations • 20 Jul 2019 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

Departing from prior spectral approaches that only compare top eigenvectors, or eigenvectors of the same order, GRAMPA first constructs a similarity matrix as a weighted sum of outer products between all pairs of eigenvectors of the two graphs, with weights given by a Cauchy kernel applied to the separation of the corresponding eigenvalues, then outputs a matching by a simple rounding procedure.

no code implementations • 5 Apr 2019 • Jan-Christian Hütter, Cheng Mao, Philippe Rigollet, Elina Robeva

Monge matrices and their permuted versions known as pre-Monge matrices naturally appear in many domains across science and engineering.

no code implementations • 25 Jun 2018 • Cheng Mao, Ashwin Pananjady, Martin J. Wainwright

Many applications, including rank aggregation, crowd-labeling, and graphon estimation, can be modeled in terms of a bivariate isotonic matrix with unknown permutations acting on its rows and/or columns.

no code implementations • 27 Feb 2018 • Cheng Mao, Ashwin Pananjady, Martin J. Wainwright

Many applications, including rank aggregation and crowd-labeling, can be modeled in terms of a bivariate isotonic matrix with unknown permutations acting on its rows and columns.

no code implementations • 28 Oct 2017 • Cheng Mao, Jonathan Weed, Philippe Rigollet

There has been a recent surge of interest in studying permutation-based models for ranking from pairwise comparison data.

no code implementations • 19 Jul 2017 • Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, Thomas A. Courtade

We show that when the assignment of items to the topology is arbitrary, these permutation-based models, unlike their parametric counterparts, do not admit consistent estimation for most comparison topologies used in practice.

no code implementations • 8 Jul 2016 • Nicolas Flammarion, Cheng Mao, Philippe Rigollet

Given a matrix the seriation problem consists in permuting its rows in such way that all its columns have the same shape, for example, they are monotone increasing.

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