分类约束随机区组模型
Assortative-Constrained Stochastic Block Models
摘要：
随机块模型（SBM）通常用于发现网络中的独立社区结构，使得社区内的连接概率高于社区之间的连接概率。然而，经典的SBM并不限于替代结构。在这项研究中，我们讨论了这个模型的影响，固有的冷漠对不确定性或不确定性，并表明，这一特点可能会导致不良后果的网络，这是预设的不确定性，但其中包含的信息量减少。为了规避这个问题，我们引入了一个约束SBM，施加强大的非线性约束，以及有效的算法方法来解决it.These约束显着提高社区恢复能力的制度，接近信息理论的阈值。它们还允许识别代表大脑皮层活动区域的网络中的结构不同的社区。
英文摘要：
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.
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