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相关论文: Algebraic Properties of Generalized Graph Laplacia…

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Laplacian operators are classical objects that are fundamental in both pure and applied mathematics and are becoming increasingly prominent in modern computational and data science fields such as applied and computational topology and…

代数拓扑 · 数学 2025-11-05 Arne Wolf , Jiyu Fan , Anthea Monod

Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely employed assumption is that of a simple common component…

统计理论 · 数学 2021-07-09 Yanli Yuan , De Wen Soh , Xiao Yang , Kun Guo , Tony Q. S. Quek

Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. It is well known that structured graph learning from observed samples is an NP-hard combinatorial problem. In…

机器学习 · 统计学 2019-09-26 Sandeep Kumar , Jiaxi Ying , Jos'e Vin'icius de M. Cardoso , Daniel P. Palomar

There is a wealth of applied problems that can be posed as a dynamical system defined on a network with both attractive and repulsive interactions. Some examples include: understanding synchronization properties of nonlinear oscillator;,…

谱理论 · 数学 2013-03-05 Jared C. Bronski , Lee DeVille

We study Laplacians associated to a graph and single out a class of such operators with special regularity properties. In the case of locally finite graphs, this class consists of all selfadjoint, non-negative restrictions of the standard…

泛函分析 · 数学 2013-05-07 Sebastian Haeseler , Matthias Keller , Daniel Lenz , Radosław Wojciechowski

Graph Laplacians computed from weighted adjacency matrices are widely used to identify geometric structure in data, and clusters in particular; their spectral properties play a central role in a number of unsupervised and semi-supervised…

谱理论 · 数学 2020-07-14 Franca Hoffmann , Bamdad Hosseini , Assad A. Oberai , Andrew M. Stuart

For a given infinite connected graph $G=(V,E)$ and an arbitrary but fixed conductance function $c$, we study an associated graph Laplacian $\Delta_{c}$; it is a generalized difference operator where the differences are measured across the…

泛函分析 · 数学 2015-06-19 Palle Jorgensen , Feng Tian

We develop a theory of ultrametric graphons as limiting objects for random networks with nested hierarchical community structure. A graphon $W:[0,1]^2\to[0,1]$ is called ultrametric if $W(x,y)=w(d(x,y))$, where $d$ is an ultrametric on…

谱理论 · 数学 2026-05-14 Ángel Alfredo Morán Ledezma

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…

机器学习 · 统计学 2011-01-31 Daniel Ting , Ling Huang , Michael Jordan

Graph Laplacians as well as related spectral inequalities and (co-)homology provide a foray into discrete analogues of Riemannian manifolds, providing a rich interplay between combinatorics, geometry and theoretical physics. We apply some…

组合数学 · 数学 2020-07-01 Yang-Hui He , Shing-Tung Yau

For a simple connected graph $ G $ of order $ n $, the normalized Laplacian is a square matrix of order $ n $, defined as $\mathcal{L}(G)= D(G)^{-\frac{1}{2}}L(G)D(G)^{-\frac{1}{2}}$, where $ D(G)^{-\frac{1}{2}} $ is the diagonal matrix…

组合数学 · 数学 2021-07-20 Bilal A. Rather , S. Pirzada , T. A. Chishti , Ahmad M. Alghamdi

Graph Laplacian learning, also known as network topology inference, is a problem of great interest to multiple communities. In Gaussian graphical models (GM), graph learning amounts to endowing covariance selection with the Laplacian…

机器学习 · 计算机科学 2024-02-14 Changhao Shi , Gal Mishne

We propose a Laplacian based on general inner product spaces, which we call the inner product Laplacian. We show the combinatorial and normalized graph Laplacians, as well as other Laplacians for hypergraphs and directed graphs, are special…

组合数学 · 数学 2025-04-16 Sinan G. Aksoy , Stephen J. Young

In this paper we generalise the results on eigenvalues and eigenvectors of unnormalized (combinatorial) Laplacian of two-dimensional grid presented by Edwards:2013 first to a grid graph of any dimension, and second also to other types of…

经典分析与常微分方程 · 数学 2019-09-02 Mieczysław A. Kłopotek

We consider the Dyson hierarchical graph $\mathcal{G}$, that is a weighted fully-connected graph, where the pattern of weights is ruled by the parameter $\sigma \in (1/2, 1]$. Exploiting the deterministic recursivity through which…

数据分析、统计与概率 · 物理学 2017-04-11 Elena Agliari , Flavia Tavani

Complex networks can model a range of different systems, from the human brain to social connections. Some of those networks have a large number of nodes and links, making it impractical to analyze them directly. One strategy to simplify…

无序系统与神经网络 · 物理学 2023-04-06 Matheus de C. Loures , Alan Albert Piovesana , José Antônio Brum

In this note, we study Laplacians on graphs for which connectivity within certain subgraphs tends to infinity. Our main focus are graphs sharing a common node set on which edge weights within certain clusters grow to infinity. As…

泛函分析 · 数学 2026-01-28 Christian Koke

Given a graph $A$ on a group $G$ and an equivalence relation $B$ on $G$, the $B$ super$A$ graph, whose vertex set is $G$ and two vertices $g$, $h$ are adjacent if and only if there exist $g^{\prime} \in[g]$ and $h^{\prime} \in[h]$ such that…

组合数学 · 数学 2024-11-27 Varun J Kaushik , Ekta , Parveen , Jitender Kumar

In this article, various aspects of Laplacian spectra of power graphs of finite cyclic, dicyclic and finite $p$-groups are studied. Algebraic connectivity of power graphs of the above groups are considered and determined completely for that…

组合数学 · 数学 2018-11-13 Ramesh Prasad Panda

We generalize the theory of critical groups from graphs to simplicial complexes. Specifically, given a simplicial complex, we define a family of abelian groups in terms of combinatorial Laplacian operators, generalizing the construction of…

组合数学 · 数学 2011-03-01 Art M. Duval , Caroline J. Klivans , Jeremy L. Martin
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