论文标题
分类受限的随机块模型
Assortative-Constrained Stochastic Block Models
论文作者
论文摘要
随机块模型（SBM）通常用于在网络中查找各种社区结构，以便社区内部连接的可能性高于社区之间。但是，经典的SBM不限于分类结构。在这项研究中，我们讨论了这种模型及其对分类性或分离性的漠不关心的含义，并表明这种特征可能会导致对具有预设的网络的不良结果，这些网络具有预期性分类，但包含降低的信息。为了解决这个问题，我们引入了一个受约束的SBM，该SBM施加了强大的分类限制，以及有效的算法方法来解决它。这些限制大大提高了接近信息理论阈值的政权中的社区恢复能力。他们还允许在代表大脑皮层活动区域的网络中确定结构不同的社区。
Stochastic block models (SBMs) are often used to find assortative community structures in networks, such that the probability of connections within communities is higher than in between communities. However, classic SBMs are not limited to assortative structures. In this study, we discuss the implications of this model-inherent indifference towards assortativity or disassortativity, and show that this characteristic can lead to undesirable outcomes for networks which are presupposedy assortative but which contain a reduced amount of information. To circumvent this issue, we introduce a constrained SBM that imposes strong assortativity constraints, along with efficient algorithmic approaches to solve it. These constraints significantly boost community recovery capabilities in regimes that are close to the information-theoretic threshold. They also permit to identify structurally-different communities in networks representing cerebral-cortex activity regions.
