Real world graphs have been observed to display a number of surprising properties. These properties include heavy-tails for inand out-degree distributions, small diameters, and a densification law [5]. These features do not arise from the classical Erdos-Renyi random graph model [1]. To address these difficulties, Kronecker Graphs were first introduced in [5] as a new method of generating graph...

In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks. We use Kronecker product to exploit the local structures within convolution and fully-connected layers, by replacing the large weight matrices by combinations of multiple Kronecker products of smaller matrices. Just as the Kronecker product is a generalizatio...

We focus here on the question of global modeling, that is, building an ordinary differential equation (ODE) of minimal dimensions which models a given multivariate time dependent data-set. We give proof of convergence of the given method. We will furthermore introduce a new method which adapts the least-squares best approximation by a Kronecker product representation [C.F. Van Loan, N. Pitsiani...

Antonian et al., (2023). PyKronecker: A Python Library for the Efficient Manipulation of Kronecker Products and Related Structures. Journal Open Source Software, 8(81), 4900, https://doi.org/10.21105/joss.04900

There are several ways for constructing (bigger) networks from smaller networks. We consider here the cartesian and the Kronecker (tensor) product networks. Our main aim is to determine a relation between the lattices of synchrony subspaces for a product network and the component networks of the product. In this sense, we show how to obtain the lattice of regular synchrony subspaces for a produ...