Kronecker Product of Tensors and Hypergraphs: Structure and Dynamics

Hypergraphs and graph products extend traditional graph theory by incorporating multi-way and coupled relationships, which are ubiquitous in real-world systems. While the Kronecker product, rooted in matrix analysis, has become a powerful tool in network science, its application has been limited to pairwise networks. In this paper, we extend the coupling of graph products to hypergraphs, enabling a system-theoretic analysis of network compositions formed via the Kronecker product of hypergraphs. We first extend the notion of the matrix Kronecker product to the tensor Kronecker product from the perspective of tensor blocks. We present various algebraic and spectral properties and express different tensor decompositions with the tensor Kronecker product. Furthermore, we study the structure and dynamics of Kronecker hypergraphs based on the tensor Kronecker product. We establish conditions that enable the analysis of the trajectory and stability of a hypergraph dynamical system by examining the dynamics of its factor hypergraphs. Finally, we demonstrate the numerical advantage of this framework for computing various tensor decompositions and spectral properties.Comment: 29 pages, 4 figures, 2 table

NFFT meets Krylov methods: Fast matrix-vector products for the graph Laplacian of fully connected networks

The graph Laplacian is a standard tool in data science, machine learning, and image processing. The corresponding matrix inherits the complex structure of the underlying network and is in certain applications densely populated. This makes computations, in particular matrix-vector products, with the graph Laplacian a hard task. A typical application is the computation of a number of its eigenvalues and eigenvectors. Standard methods become infeasible as the number of nodes in the graph is too large. We propose the use of the fast summation based on the nonequispaced fast Fourier transform (NFFT) to perform the dense matrix-vector product with the graph Laplacian fast without ever forming the whole matrix. The enormous flexibility of the NFFT algorithm allows us to embed the accelerated multiplication into Lanczos-based eigenvalues routines or iterative linear system solvers and even consider other than the standard Gaussian kernels. We illustrate the feasibility of our approach on a number of test problems from image segmentation to semi-supervised learning based on graph-based PDEs. In particular, we compare our approach with the Nystr\"om method. Moreover, we present and test an enhanced, hybrid version of the Nystr\"om method, which internally uses the NFFT.Comment: 28 pages, 9 figure

On the Complexity of Recognizing S-composite and S-prime Graphs

S-prime graphs are graphs that cannot be represented as nontrivial subgraphs of nontrivial Cartesian products of graphs, i.e., whenever it is a subgraph of a nontrivial Cartesian product graph it is a subgraph of one the factors. A graph is S-composite if it is not S-prime. Although linear time recognition algorithms for determining whether a graph is prime or not with respect to the Cartesian product are known, it remained unknown if a similar result holds also for the recognition of S-prime and S-composite graphs. In this contribution the computational complexity of recognizing S-composite and S-prime graphs is considered. Klav{\v{z}}ar \emph{et al.} [\emph{Discr.\ Math.} \textbf{244}: 223-230 (2002)] proved that a graph is S-composite if and only if it admits a nontrivial path-$k$-coloring. The problem of determining whether there exists a path-$k$-coloring for a given graph is shown to be NP-complete even for $k=2$. This in turn is utilized to show that determining whether a graph is S-composite is NP-complete and thus, determining whether a graph is S-prime is CoNP-complete. Many other problems are shown to be NP-hard, using the latter results