Multilayer Artificial Benchmark for Community Detection (mABCD)
This work addresses a gap in network science for researchers needing synthetic multilayer graph models, though it is incremental as it builds on the existing ABCD framework.
The authors tackled the lack of synthetic models for generating multilayer networks by introducing mABCD, a variant of the ABCD model, which is flexible in modeling internal layer structures and inter-layer dependencies, and demonstrated its applicability to spreading phenomena analysis.
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.