2011/06/08 by David F. Gleich, Art B. Owen, Gleich, David F. +1
Computer Science · Mathematics · Physics and Astronomy · #63P99 #91D30 #Complex Network Analysis Techniques #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning (stat.ML) #Scientific Research and Discoveries #Social and Information Networks (cs.SI) #cs.SI #msc:63P99 #msc:91D30 #stat.ML
paper · pdf · doi:10.48550/arxiv.1106.1674
22 pages, 4 figures
arxiv created 2011/06/08 · openalex publication_date 2011/06/08 · arxiv updated 2011/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.