提出可生成多层网络的社区检测基准模型，灵活建模层内与层间关系。
Multilayer Artificial Benchmark for Community Detection (mABCD)
- 基于ABCD模型扩展，支持多层网络的层内结构与层间依赖灵活建模。
- 通过高效Julia实现，支持大规模网络生成与分析。
- 适用于复杂系统中的传播现象研究，如信息扩散、疾病传播等。
网络科学中持续存在的挑战之一是开发各种合成图模型以支持后续分析。其中最突出的框架之一是社区检测人工基准（ABCD）模型，这是一种具有社区结构和度分布及社区规模幂律分布的随机图模型。该模型生成的图类似于著名的LFR模型，但速度更快、更易解释，并可进行解析分析。本文利用ABCD的底层机制，引入其变体mABCD，从而填补了能够生成多层网络的模型空白。所提方法的独特性在于其在两个层面均具备灵活性：单个层内的内部结构以及层间的依赖关系，使网络成为连贯整体而非松散耦合的图集合。除了概念性描述外，本文还提供了其高效Julia实现的全面分析。最后，展示了mABCD在复杂系统领域最突出的问题之一——传播现象分析中的适用性。
原文摘要 · Abstract (English)
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.
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