A generalized configuration model with triadic closure

TL;DR

This paper introduces a generalized network model with triadic closure that accurately replicates key properties of real-world networks, including clustering, degree correlation, community structure, and influence spread.

Contribution

The paper presents a novel generalized configuration model with triadic closure, analytically derives its properties, and demonstrates its effectiveness in modeling real-world network features.

Findings

GCTC model matches real networks in clustering and degree correlation

GCTC performs comparably to benchmark models in community detection

Influence spread in GCTC closely resembles real-world networks

Abstract

In this paper we present a generalized configuration model with random triadic closure (GCTC). This model possesses five fundamental properties: large clustering coefficient, power law degree distribution, short path length, non-zero Pearson degree correlation, and existence of community structures. We analytically derive the Pearson degree correlation coefficient and the clustering coefficient of the proposed model. We select a few datasets of real-world networks. By simulation, we show that the GCTC model matches very well with the datasets in terms of Pearson degree correlations and clustering coefficients. We also test three well-known community detection algorithms on our model, the datasets and other three prevalent benchmark models. We show that the GCTC model performs equally well as the other three benchmark models. Finally, we perform influence diffusion on the GCTC model using the independent cascade model and the linear threshold model. We show that the influence spreads of the GCTC model are much closer to those of the datasets than the other benchmark models. This suggests that the GCTC model is a suitable tool to study network science problems where degree correlation or clustering plays an important role.