Algorithms for Generating Large-scale Clustered Random Graphs

TL;DR

This paper introduces an advanced algorithm for generating large-scale clustered random graphs that preserve key properties of real social networks, enabling better structural comparisons and analysis.

Contribution

A generalized version of Gleeson's algorithm is proposed to efficiently generate large-scale clustered graphs maintaining key network metrics.

Findings

Preserves number of nodes and edges.

Maintains global clustering coefficient.

Improves randomness evaluation and computation time.

Abstract

Real social networks are often compared to random graphs in order to assess whether their typological structure could be the result of random processes. However, an Erd\H{o}s-R\'enyi random graph in large scale is often lack of local structure beyond the dyadic level and as a result we need to generate the clustered random graph instead of the simple random graph to compare the local structure at the triadic level. In this paper a generalized version of Gleeson's algorithm is advanced to generate a clustered random graph in large-scale which persists the number of nodes |V|, the number of edges |E|, and the global clustering coefficient C{\Delta} as in the real network. And it also has advantages in randomness evaluation and computation time when comparing with the existing algorithms.