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Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd number of negative…

Machine Learning · Computer Science 2025-06-03 Haruki Yokota , Hiroshi Higashi , Yuichi Tanaka , Gene Cheung

This paper characterizes the graphical properties of an optimal topology with minimal Laplacian energy under the constraint of fixed numbers of vertices and edges, and devises an algorithm to construct such connected optimal graphs. These…

Optimization and Control · Mathematics 2024-03-26 Susie Lu , Ji Liu

We revisit the calculation of height correlations in the two-dimensional Abelian sandpile model by taking advantage of a technique developed recently by Kenyon and Wilson. The formalism requires to equip the usual graph Laplacian,…

Statistical Mechanics · Physics 2017-12-25 Adrien Poncelet , Philippe Ruelle

We propose an augmented Lagrangian-based preconditioner to accelerate the convergence of Krylov subspace methods applied to linear systems of equations with a block three-by-three structure such as those arising from mixed finite element…

Numerical Analysis · Mathematics 2023-10-26 Fatemeh P. A. Beik , Michele Benzi

Multiplex graphs, characterised by their layered structure, exhibit informative interdependencies within layers that are crucial for understanding complex network dynamics. Quantifying the interaction and shared information among these…

Statistics Theory · Mathematics 2024-05-24 Anda Skeja , Sofia C. Olhede

We analyze the spectral clustering procedure for identifying coarse structure in a data set $x_1, \dots, x_n$, and in particular study the geometry of graph Laplacian embeddings which form the basis for spectral clustering algorithms. More…

Spectral Theory · Mathematics 2019-01-31 Nicolas Garcia Trillos , Franca Hoffmann , Bamdad Hosseini

We present a geometric multilevel optimization approach that smoothly incorporates box constraints. Given a box constrained optimization problem, we consider a hierarchy of models with varying discretization levels. Finer models are…

Optimization and Control · Mathematics 2024-04-23 Sebastian Müller , Stefania Petra , Matthias Zisler

Several invariants of polarized metrized graphs and their applications in Arithmetic Geometry are studied recently. In this paper, we give fast algorithms to compute these invariants by expressing them in terms of the discrete Laplacian…

Number Theory · Mathematics 2012-02-22 Zubeyir Cinkir

We study the problem of generating graphs with prescribed degree sequences for bipartite, directed, and undirected networks. We first propose a sequential method for bipartite graph generation and establish a necessary and sufficient…

Methodology · Statistics 2026-03-13 Tong Sun , Jianshu Hao , Michael C. Fu , Guangxin Jiang

We consider a non self-adjoint Laplacian on a directed graph with non symmetric weights on edges. We give a criterion for the m-accretiveness and the m-sectoriality of this Laplacian. Our results are based on a comparison of this operator…

Spectral Theory · Mathematics 2019-06-19 Colette Anné , Marwa Balti , Nabila Torki-Hamza

We prove two mixed versions of the Discrete Nodal Theorem of Davies et. al. [3] for bounded degree graphs, and for three-connected graphs of fixed genus $g$. Using this we can show that for a three-connected graph satisfying a certain…

Combinatorics · Mathematics 2015-12-02 Yong Lin , Gabor Lippner , Dan Mangoubi , Shing-Tung Yau

Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumption and generalize the analysis of graph Laplacians to include…

Machine Learning · Statistics 2011-01-31 Daniel Ting , Ling Huang , Michael Jordan

In this paper we present complexity certification results for a distributed Augmented Lagrangian (AL) algorithm used to solve convex optimization problems involving globally coupled linear constraints. Our method relies on the Accelerated…

Optimization and Control · Mathematics 2018-01-16 Soomin Lee , Nikolaos Chatzipanagiotis , Michael M. Zavlanos

Recent work in theoretical computer science and scientific computing has focused on nearly-linear-time algorithms for solving systems of linear equations. While introducing several novel theoretical perspectives, this work has yet to lead…

Numerical Analysis · Computer Science 2010-05-19 Petros Drineas , Michael W. Mahoney

We adapt multilevel, force-directed graph layout techniques to visualizing dynamic graphs in which vertices and edges are added and removed in an online fashion (i.e., unpredictably). We maintain multiple levels of coarseness using a…

Graphics · Computer Science 2007-12-11 Todd L. Veldhuizen

We introduce a level set based approach to Bayesian geometric inverse problems. In these problems the interface between different domains is the key unknown, and is realized as the level set of a function. This function itself becomes the…

Methodology · Statistics 2015-04-02 Marco A. Iglesias , Yulong Lu , Andrew M. Stuart

In this paper algebraic and combinatorial properties and a computation of the number of the spanning trees are developed for certain graphs. To this purpose, an original method, independent of the spectrum of the Laplacian matrix associated…

Combinatorics · Mathematics 2024-04-01 Maurizio Imbesi , Monica La Barbiera , Santo Saraceno

We study two different types of gluing for graphs: interface (obtained by choosing a common subgraph as the gluing component) and bridge gluing (obtained by adding a set of edges to the given subgraphs). We introduce formulae for computing…

Mathematical Physics · Physics 2018-05-07 Ivan Contreras , Michael Toriyama , Chengzheng Yu

Following Hartigan, a cluster is defined as a connected component of the t-level set of the underlying density, i.e., the set of points for which the density is greater than t. A clustering algorithm which combines a density estimate with…

Machine Learning · Statistics 2010-02-12 Bruno Pelletier , Pierre Pudlo

Bi-stochastic normalization provides an alternative normalization of graph Laplacians in graph-based data analysis and can be computed efficiently by Sinkhorn-Knopp (SK) iterations. This paper proves the convergence of bi-stochastically…

Statistics Theory · Mathematics 2024-07-19 Xiuyuan Cheng , Boris Landa