论文标题
系统评估随机块模型的经验网络质量
Systematic assessment of the quality of fit of the stochastic block model for empirical networks
论文作者
论文摘要
我们对275个经验网络的随机块模型（SBM）的拟合质量进行系统分析，这些经验网络涵盖了广泛的域和大小幅度的顺序。根据一组网络描述符，我们采用后验预测模型检查作为评估拟合质量的标准，涉及将推论模型与经验网络的网络进行比较。我们观察到，SBM能够为所考虑的大多数网络提供准确的描述，但没有使所有建模要求饱和。特别是，拥有大直径和慢速混合随机步行的网络往往会被SBM描述。但是，与通常假定的相反，在许多情况下，SBM可以很好地描述具有很高三角形的网络。我们证明，简单的网络描述符可用于评估SBM是否可以提供足够准确的表示形式，并有可能指向可能的模型扩展，这些模型扩展可以系统地改善此类模型的表现力。
We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to assess the quality of fit, which involves comparing networks generated by the inferred model with the empirical network, according to a set of network descriptors. We observe that the SBM is capable of providing an accurate description for the majority of networks considered, but falls short of saturating all modeling requirements. In particular, networks possessing a large diameter and slow-mixing random walks tend to be badly described by the SBM. However, contrary to what is often assumed, networks with a high abundance of triangles can be well described by the SBM in many cases. We demonstrate that simple network descriptors can be used to evaluate whether or not the SBM can provide a sufficiently accurate representation, potentially pointing to possible model extensions that can systematically improve the expressiveness of this class of models.
