Internet Mathematics

Moment-Based Estimation of Stochastic Kronecker Graph Parameters

David F. Gleich and Art B. Owen

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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.

Article information

Internet Math., Volume 8, Number 3 (2012), 232-256.

First available in Project Euclid: 21 August 2012

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Gleich, David F.; Owen, Art B. Moment-Based Estimation of Stochastic Kronecker Graph Parameters. Internet Math. 8 (2012), no. 3, 232--256.

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