scispace - formally typeset
Open AccessPosted Content

Spectra of Uniform Hypergraphs

Reads0
Chats0
TLDR
In this article, a spectral theory of hypergraphs is presented, which closely parallels Spectral Graph Theory, and it is possible to define eigenvalues of a hypermatrix via its characteristic polynomial as well as variationally.
Abstract
We present a spectral theory of hypergraphs that closely parallels Spectral Graph Theory. A number of recent developments building upon classical work has led to a rich understanding of "hyperdeterminants" of hypermatrices, a.k.a. multidimensional arrays. Hyperdeterminants share many properties with determinants, but the context of multilinear algebra is substantially more complicated than the linear algebra required to address Spectral Graph Theory (i.e., ordinary matrices). Nonetheless, it is possible to define eigenvalues of a hypermatrix via its characteristic polynomial as well as variationally. We apply this notion to the "adjacency hypermatrix" of a uniform hypergraph, and prove a number of natural analogues of basic results in Spectral Graph Theory. Open problems abound, and we present a number of directions for further study.

read more

Citations
More filters
Posted Content

On spectral hypergraph theory of the adjacency tensor

TL;DR: In this paper, the adjacency tensor of a uniform multi-hypergraph is studied and conditions for the largest positive eigenvalue corresponding to a strictly positive vector are given.
Proceedings ArticleDOI

Hypergraph Markov Operators, Eigenvalues and Approximation Algorithms

TL;DR: It is shown that there can be no linear operator for hypergraphs whose spectra captures hypergraph expansion in a Cheeger-like manner, and the Laplacian operator introduced is non-linear, and thus computing its eigenvalues exactly is intractable.
Posted Content

The Spectral Theory of Tensors (Rough Version)

Liqun Qi
TL;DR: The spectral theory of tensors is an important part of numerical multi-linear algebra, or tensor computation as mentioned in this paper, and has attracted attention of mathematicians from different disciplines, such as automatic control, statistical data analysis, optimization, magnetic resonance imaging, solid mechanics, quantum physics, higher order Markov chains, spectral hypergraph theory, Finsler geometry, etc.
Posted Content

Eigenvalues and Linear Quasirandom Hypergraphs

TL;DR: In this paper, the authors connect previous notions on linear hypergraph quasirandomness of Kohayakawa-R\"odl-Skokan and Conlon-H\`{a}n-Person-Schacht and the spectral approach of Friedman-Wigderson.
Journal ArticleDOI

Spectral Radius on Linear $r$-Graphs without Expanded $K_{r+1}$

TL;DR: In this article , it was shown that the spectral radius of the adjacency tensor of a transversal hypergraph is no more than Θ(n 2 ) for sufficiently large polygonal numbers.
References
More filters
Book

Using Algebraic Geometry

TL;DR: The Berlekamp-Massey-Sakata Decoding Algorithm is used for solving Polynomial Equations and for computations in Local Rings.
Proceedings ArticleDOI

Singular values and eigenvalues of tensors: a variational approach

TL;DR: A theory of eigenvalues, eigenvectors, singular values, and singular vectors for tensors based on a constrained variational approach much like the Rayleigh quotient for symmetric matrix eigen values is proposed.
Journal ArticleDOI

Perron–Frobenius theorem for nonnegative multilinear forms and extensions

TL;DR: In this article, the convergence rate of the power algorithm to the unique normalized eigenvector for polynomial maps with nonnegative coefficients was shown to be linear in the number of coefficients.
Journal ArticleDOI

Algebraic connectivity of an even uniform hypergraph

TL;DR: Algebraic connectivity of an even uniform hypergraph based on Z-eigenvalues of the corresponding Laplacian tensor is introduced and its connections with edge connectivity and vertex connectivity are discussed.
Journal ArticleDOI

On Unicursal Paths in a Network of Degree 4

TL;DR: In this paper, the authors present the Unicursal Paths in a Network of Degree 4, a network of degree 4, where each degree corresponds to a node in a graph.