Edge Based Stochastic Block Model Statistical Inference
Edge Based Stochastic Block Model Statistical Inference
复制标题
基于边缘的随机块模型统计推断
DOI：
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发表时间：
2021
期刊：
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通讯作者：
C. Robardet
中科院分区：
文献类型：
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作者：
Louis Duvivier;Rémy Cazabet;C. Robardet
图中的社区检测通常依赖于ad hoc算法，没有明确的规范，他们定义为最好的节点分区，这导致无法解释的社区。随机块模型（SBM）提供了一个框架来严格定义社区，并使用统计推断方法来区分结构和随机波动来检测它们。在本文中，我们介绍了另一种定义的SBM边缘采样的基础上。从这个定义中，我们推导出一个质量函数来统计推断用于生成给定图的节点划分。然后我们在合成图和扎卡里空手道俱乐部网络上测试它。
Community detection in graphs often relies on ad hoc algorithms with no clear specification about the node partition they define as the best, which leads to uninterpretable communities. Stochastic block models (SBM) offer a framework to rigorously define communities, and to detect them using statistical inference method to distinguish structure from random fluctuations. In this paper, we introduce an alternative definition of SBM based on edge sampling. We derive from this definition a quality function to statistically infer the node partition used to generate a given graph. We then test it on synthetic graphs, and on the zachary karate club network.