论文标题
基于碎裂凝血的混合成员随机块模型
Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel
论文作者
论文摘要
提出了混合成员随机块模型（MMSB）作为最新的贝叶斯关系方法之一，适合学习网络数据基础的复杂隐藏结构。但是，当前MMSB的表述遇到了以下两个问题：（1），先前的信息〜（例如，实体的社区结构信息）不能很好地嵌入建模中； （2）文献中无法很好地描述社区进化。因此，我们提出了基于非参数碎片凝结基于混合成员的随机块模型（FCMMSB）。我们的模型执行基于实体的聚类，以捕获实体和基于链接的聚类的社区信息，以同时得出链接的组信息。此外，提出的模型使用离散的碎片凝结过程（DFCP）来渗透网络结构和模型社区演变，并由社区的外观和消失表现出来。通过将社区结构与组兼容性矩阵整合在一起，我们得出了MMSB的广义版本。使用Polya Gamma（PG）方法进行有效的Gibbs采样方案进行后推断。我们验证了合成和现实世界数据的模型。
The Mixed-Membership Stochastic Blockmodel~(MMSB) is proposed as one of the state-of-the-art Bayesian relational methods suitable for learning the complex hidden structure underlying the network data. However, the current formulation of MMSB suffers from the following two issues: (1), the prior information~(e.g. entities' community structural information) can not be well embedded in the modelling; (2), community evolution can not be well described in the literature. Therefore, we propose a non-parametric fragmentation coagulation based Mixed Membership Stochastic Blockmodel (fcMMSB). Our model performs entity-based clustering to capture the community information for entities and linkage-based clustering to derive the group information for links simultaneously. Besides, the proposed model infers the network structure and models community evolution, manifested by appearances and disappearances of communities, using the discrete fragmentation coagulation process (DFCP). By integrating the community structure with the group compatibility matrix we derive a generalized version of MMSB. An efficient Gibbs sampling scheme with Polya Gamma (PG) approach is implemented for posterior inference. We validate our model on synthetic and real world data.
