论文标题
人工基准用于社区检测（ABCD）：具有社区结构的快速随机图模型
Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure
论文作者
论文摘要
从业者感兴趣的当前大多数复杂网络都具有某种社区结构，在理解这些网络的属性中起着重要作用。此外，许多用于复杂网络开发的机器学习算法和工具试图利用社区的存在来提高其性能或速度。结果，有许多竞争算法用于检测大型网络中的社区。不幸的是，这些算法通常非常敏感，因此对于给定的，可以不断变化的现实世界网络，它们无法对其进行微调。因此，重要的是要针对各种情况测试这些算法，这些算法只能使用具有内置社区结构，幂律度分布和其他在复杂网络中观察到的典型属性的合成图进行。 LFR Graph Generator是生成人工网络的标准和广泛使用的方法。不幸的是，该模型具有一定的可扩展性限制，理论上分析它是一项挑战。最后，混合参数$μ$是指导社区强度的模型的主要参数，具有非明显的解释，因此可能导致不自然定义的网络。在本文中，我们提供了一个替代性随机图模型，其学位和社区规模的社区结构和幂律分布，这是社区检测的人工基准（ABCD）。我们表明，新模型解决了上述三个问题以及更多问题。结论是这些模型产生了可比的图形，但是ABCD快速，简单，可以轻松调整以允许用户在两个极端之间进行平稳的过渡：纯净（独立）社区和无社区结构的随机图。
Most of the current complex networks that are of interest to practitioners possess a certain community structure that plays an important role in understanding the properties of these networks. Moreover, many machine learning algorithms and tools that are developed for complex networks try to take advantage of the existence of communities to improve their performance or speed. As a result, there are many competing algorithms for detecting communities in large networks. Unfortunately, these algorithms are often quite sensitive and so they cannot be fine-tuned for a given, but a constantly changing, real-world network at hand. It is therefore important to test these algorithms for various scenarios that can only be done using synthetic graphs that have built-in community structure, power-law degree distribution, and other typical properties observed in complex networks. The standard and extensively used method for generating artificial networks is the LFR graph generator. Unfortunately, this model has some scalability limitations and it is challenging to analyze it theoretically. Finally, the mixing parameter $μ$, the main parameter of the model guiding the strength of the communities, has a non-obvious interpretation and so can lead to unnaturally-defined networks. In this paper, we provide an alternative random graph model with community structure and power-law distribution for both degrees and community sizes, the Artificial Benchmark for Community Detection (ABCD). We show that the new model solves the three issues identified above and more. The conclusion is that these models produce comparable graphs but ABCD is fast, simple, and can be easily tuned to allow the user to make a smooth transition between the two extremes: pure (independent) communities and random graph with no community structure.
