基于矩的随机Kronecker图参数估计
Moment based estimation of stochastic Kronecker graph parameters
摘要：
随机Kronecker图为大型稀疏现实世界图提供了一个简约模型。他们可以使用三个或四个参数来指定一个大型随机图的分布。然而，这些参数在具体应用中证明难以选择。这篇文章着眼于矩估计的方法，计算上比极大似然简单得多。估计器速度很快，在我们的示例中，它们通常会产生Kronecker参数，其预期特征计数比我们从KronFit获得的更接近给定图。这种改进在图中三角形的数量方面尤为突出。
英文摘要：
Stochastic Kronecker graphs supply a parsimonious model for large sparse real world graphs. They can specify the distribution of a large random graph using only three or four parameters. Those parameters have however proved difficult to choose in specific applications. This article looks at method of moments estimators that are computationally much simpler than maximum likelihood. The estimators are fast and in our examples, they typically yield Kronecker parameters with expected feature counts closer to a given graph than we get from KronFit. The improvement was especially prominent for the number of triangles in the graph.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
