Evolving complex networks with conserved clique distributions

TL;DR

This paper introduces a hierarchical algorithm for generating complex networks with specific clique distributions, allowing for random or preferential attachment, and evaluates their properties against real-world networks.

Contribution

The authors present a novel hierarchical algorithm to generate graphs with a fixed clique distribution, incorporating randomness or preferential attachment mechanisms.

Findings

Generated graphs match target clique distributions.

Networks exhibit realistic degree distributions.

Comparison shows similarity to real-world networks.

Abstract

We propose and study a hierarchical algorithm to generate graphs having a predetermined distribution of cliques, the fully connected subgraphs. The construction mechanism may be either random or incorporate preferential attachment. We evaluate the statistical properties of the graphs generated, such as the degree distribution and network diameters, and compare them to some real-world graphs.