Multilayer Artificial Benchmark for Community Detection (mABCD)

TL;DR

This paper introduces mABCD, a multilayer extension of the ABCD model, providing a flexible, interpretable synthetic graph generator for community detection and spreading phenomena analysis.

Contribution

The paper presents mABCD, a novel multilayer network model that enhances the original ABCD framework with layered structure and inter-layer dependencies, enabling more realistic synthetic network generation.

Findings

mABCD can generate multilayer networks with customizable internal and inter-layer structures.

The implementation of mABCD is efficient and available in Julia.

Application to spreading phenomena demonstrates the model's practical utility.

Abstract

One of the most persistent challenges in network science is the development of various synthetic graph models to support subsequent analyses. Among the most notable frameworks addressing this issue is the Artificial Benchmark for Community Detection (ABCD) model, a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs similar to the well-known LFR model but it is faster, more interpretable, and can be investigated analytically. In this paper, we use the underlying ingredients of ABCD and introduce its variant, mABCD, thereby addressing the gap in models capable of generating multilayer networks. The uniqueness of the proposed approach lies in its flexibility at both levels of modelling: the internal structure of individual layers and the inter-layer dependencies, which together make the network a coherent structure rather than a collection of loosely coupled graphs. In addition to the conceptual description of the framework, we provide a comprehensive analysis of its efficient Julia implementation. Finally, we illustrate the applicability of mABCD to one of the most prominent problems in the area of complex systems: spreading phenomena analysis.