Searcharxiv⌕ Search

arXiv subjects

Victory Idowu

Publications and source records attributed to Victory Idowu.

3 recordsLinked to original sources
arXiv2025

Generating Networks to Target Assortativity via Archimedean Copula Graphons

We develop an approach to generate random graphs to a target level of assortativity by using copula structures in graphons. Unlike existing random graph generators, we do not use rewiring or binning approaches to generate the desired random graph. Instead, we connect Archimedean bivariate copulas to graphons in order to produce flexible models that can generate random graphs to target assortativity. We propose three models that use the copula distribution function, copula density function and their mixed tensor product to produce networks. We express the assortativity coefficient in terms of homomorphism densities. Establishing this relationship forges a connection between the parameter of the copula and the frequency of subgraphs in the generated network. Therefore, our method attains a desired the subgraph distribution as well as the target assortativity. We establish the homomorphism densities and assortativity coefficient for each of the models. Numerical examples demonstrate the ability of the proposed models to produce graphs with different levels of assortativity.

cs.SI↗
arXiv2025

On tail dependence parameters for non-continuous and autocorrelated margins

Tail dependence plays an essential role in the characterization of joint extreme events in multivariate data. However, most standard tail dependence parameters assume continuous margins. This note presents a form of tail dependence suitable for non-continuous and discrete margins. We derive a representation of tail dependence based on the volume of a copula and prove its properties. We utilize a bivariate regular variation to show that our new metric is consistent with the standard tail dependence parameters on continuous margins. We further define tail dependence on autocorrelated margins where the tail dependence parameter examine lagged correlation on the sample.

math.ST↗