Moment-Based Estimation of Stochastic Kronecker Graph Parameters

Q3 Mathematics
D. Gleich, A. Owen
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引用次数: 39

Abstract

Abstract 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 is especially prominent for the number of triangles in the graph.
基于矩的随机Kronecker图参数估计
随机Kronecker图为现实世界的大型稀疏图提供了一种简约模型。它们可以只使用三到四个参数来指定大型随机图的分布。然而,这些参数在具体应用中很难选择。本文着眼于矩法估计,它在计算上比最大似然简单得多。估计器速度很快,在我们的例子中,它们通常产生的Kronecker参数的预期特征计数比我们从KronFit中得到的更接近给定图。这种改进对于图中三角形的数量尤其突出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Internet Mathematics
Internet Mathematics Mathematics-Applied Mathematics
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