Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Gaudio, Julia"'
Autor:
Gaudio, Julia, Liu, Heming
We consider the problem of exact community recovery in the Labeled Stochastic Block Model (LSBM) with $k$ communities, where each pair of vertices is associated with a label from the set $\{0,1, \dots, L\}$. A pair of vertices from communities $i,j$
Externí odkaz:
http://arxiv.org/abs/2408.13075
In this paper, we propose a family of label recovery problems on weighted Euclidean random graphs. The vertices of a graph are embedded in $\mathbb{R}^d$ according to a Poisson point process, and are assigned to a discrete community label. Our goal i
Externí odkaz:
http://arxiv.org/abs/2407.11163
Autor:
Gaudio, Julia, Joshi, Nirmit
In this paper, we study the problem of exact community recovery in general, two-community block models considering both Bernoulli and Gaussian matrix models, capturing the Stochastic Block Model, submatrix localization, and $\mathbb{Z}_2$-synchroniza
Externí odkaz:
http://arxiv.org/abs/2406.13075
We study the problem of exact community recovery in the Geometric Stochastic Block Model (GSBM), where each vertex has an unknown community label as well as a known position, generated according to a Poisson point process in $\mathbb{R}^d$. Edges are
Externí odkaz:
http://arxiv.org/abs/2307.11196
We propose a simple and efficient local algorithm for graph isomorphism which succeeds for a large class of sparse graphs. This algorithm produces a low-depth canonical labeling, which is a labeling of the vertices of the graph that identifies its is
Externí odkaz:
http://arxiv.org/abs/2211.16454
Autor:
Kurbanzade, Ali Kaan, Gaudio, Julia
In typical applications of facility location problems, the location of demand is assumed to be an input to the problem. The demand may be fixed or dynamic, but ultimately outside the optimizers control. In contrast, there are settings, especially in
Externí odkaz:
http://arxiv.org/abs/2211.12587
Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algor
Externí odkaz:
http://arxiv.org/abs/2210.05893
Autor:
Gaudio, Julia, Joshi, Nirmit
Community detection is a fundamental problem in network science. In this paper, we consider community detection in hypergraphs drawn from the $hypergraph$ $stochastic$ $block$ $model$ (HSBM), with a focus on exact community recovery. We study the per
Externí odkaz:
http://arxiv.org/abs/2208.12227
We consider the problem of learning latent community structure from multiple correlated networks. We study edge-correlated stochastic block models with two balanced communities, focusing on the regime where the average degree is logarithmic in the nu
Externí odkaz:
http://arxiv.org/abs/2203.15736
Spectral algorithms are an important building block in machine learning and graph algorithms. We are interested in studying when such algorithms can be applied directly to provide optimal solutions to inference tasks. Previous works by Abbe, Fan, Wan
Externí odkaz:
http://arxiv.org/abs/2203.11847