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Consider a semigraph $G=(V,\,E)$; in this paper, we study the eigenvalues of the Laplacian matrix of $G$. We show that the Laplacian of $G$ is positive semi-definite, and $G$ is connected if and only if $\lambda_2 >0.$ Along the similar…

Combinatorics · Mathematics 2023-07-10 Pralhad M. Shinde

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…

Combinatorics · Mathematics 2025-04-16 Sinan G. Aksoy , Stephen J. Young

In this paper, we characterize all connected graphs with exactly three distinct normalized Laplacian eigenvalues of which one is equal to $1$, determine all connected bipartite graphs with at least one vertex of degree $1$ having exactly…

Combinatorics · Mathematics 2017-03-28 Xueyi Huang , Qiongxiang Huang

Using spectral embedding based on the signless Laplacian, we obtain bounds on the spectrum of transition matrices on graphs. As a consequence, we bound return probabilities and the uniform mixing time of simple random walk on graphs. In…

Probability · Mathematics 2023-01-03 Zhi-Feng Wei

This paper develops a framework for privatizing the spectrum of the graph Laplacian of an undirected graph using differential privacy. We consider two privacy formulations. The first obfuscates the presence of edges in the graph and the…

Optimization and Control · Mathematics 2022-11-29 Calvin Hawkins , Bo Chen , Kasra Yazdani , Matthew Hale

Here we study the spectral properties of an underlying weighted graph of a non-uniform hypergraph by introducing different connectivity matrices, such as adjacency, Laplacian and normalized Laplacian matrices. We show that different…

Combinatorics · Mathematics 2019-03-28 Anirban Banerjee

We derive the determinant of the Laplacian for the Hanoi networks and use it to determine their number of spanning trees (or graph complexity) asymptotically. While spanning trees generally proliferate with increasing average degree, the…

Statistical Mechanics · Physics 2015-10-08 Stefan Boettcher , Shanshan Li

We propose a framework for the visualization of directed networks relying on the eigenfunctions of the magnetic Laplacian, called here Magnetic Eigenmaps. The magnetic Laplacian is a complex deformation of the well-known combinatorial…

Social and Information Networks · Computer Science 2017-10-25 Michaël Fanuel , Carlos M. Alaíz , Ángela Fernández , Johan A. K. Suykens

Many natural and social systems develop complex networks, that are usually modelled as random graphs. The eigenvalue spectrum of these graphs provides information about their structural properties. While the semi-circle law is known to…

Statistical Mechanics · Physics 2009-11-07 Illes J. Farkas , Imre Derenyi , Albert-Laszlo Barabasi , Tamas Vicsek

We introduce a graph renormalization procedure based on the coarse-grained Laplacian, which generates reduced-complexity representations for characteristic scales identified through the spectral gap. This method retains both diffusion…

Statistical Mechanics · Physics 2024-11-20 M. Schmidt , F. Caccioli , T. Aste

We prove a central limit theorem for the components of the eigenvectors corresponding to the $d$ largest eigenvalues of the normalized Laplacian matrix of a finite dimensional random dot product graph. As a corollary, we show that for…

Machine Learning · Statistics 2016-07-29 Minh Tang , Carey E. Priebe

Sampling methods for graph signals in the graph spectral domain are presented. Though conventional sampling of graph signals can be regarded as sampling in the graph vertex domain, it does not have the desired characteristics in regard to…

Information Theory · Computer Science 2018-06-13 Yuichi Tanaka

We compare the spectrum and the localisation properties of the eigenmodes of the Laplacian and the adjacency matrix of 2D random geometric graphs, using numerical diagonalization of these matrices for different system sizes and…

Disordered Systems and Neural Networks · Physics 2026-04-01 Luca Schaefer , Barbara Drossel

Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare. To circumvent this problem and learn on graphs, graph feature representation is required. A good…

Machine Learning · Computer Science 2019-12-03 Edouard Pineau

In an era of unprecedented deluge of (mostly unstructured) data, graphs are proving more and more useful, across the sciences, as a flexible abstraction to capture complex relationships between complex objects. One of the main challenges…

Disordered Systems and Neural Networks · Physics 2016-10-17 Alaa Saade

We study how the spectral gap of the normalized Laplacian of a random graph changes when an edge is added to or removed from the graph. There are known examples of graphs where, perhaps counterintuitively, adding an edge can decrease the…

Combinatorics · Mathematics 2015-06-23 Ronen Eldan , Miklós Rácz , Tselil Schramm

In this paper, we investigate some relations between the invariants (including vertex and edge connectivity and forwarding indices) of a graph and its Laplacian eigenvalues. In addition, we present a sufficient condition for the existence…

Combinatorics · Mathematics 2014-07-23 Rong-Ying Pan , Jing Yan , Xiao-Dong Zhang

We provide explicit upper bounds for the eigenvalues of the Laplacian on a finite metric tree subject to standard vertex conditions. The results include estimates depending on the average length of the edges or the diameter. In particular,…

Spectral Theory · Mathematics 2016-07-28 Jonathan Rohleder

This paper deals with spectral graph theory issues related to questions of monotonicity and comparison of eigenvalues. We consider finite directed graphs with non symmetric edge weights and we introduce a special self-adjoint operator as…

Spectral Theory · Mathematics 2019-04-25 Marwa Balti

We perform an extensive investigation of the localization properties of the eigenmodes of the Laplace and adjacency matrix for one-dimensional random geometric graphs. We evaluate the density of states, the probability distribution of the…

Disordered Systems and Neural Networks · Physics 2025-08-27 Luca Schaefer , Barbara Drossel