Random Irregular Block-hierarchical Networks: Algorithms for Computation of Main Properties

TL;DR

This paper introduces a new class of random irregular block-hierarchical networks and presents efficient algorithms for their generation and analysis, enabling better understanding of their topological properties.

Contribution

The paper defines the class of random irregular block-hierarchical networks and develops more efficient algorithms for their generation and property calculation.

Findings

Algorithms outperform existing methods in speed and memory efficiency.

The algorithms facilitate detailed topological analysis of the networks.

Implementation supports statistical and topological studies of complex networks.

Abstract

In this paper, the class of random irregular block-hierarchical networks is defined and algorithms for generation and calculation of network properties are described. The algorithms presented for this class of networks are more efficient than known algorithms both in computation time and memory usage and can be used to analyze topological properties of such networks. The algorithms are implemented in the system created by the authors for the study of topological and statistical properties of random networks.