A tensor version of Kronecker-graph denoising and parameter inference is presented, but it reuses prior matrix results with a layer index and contains model-definition and proof gaps.
Emergence of scaling in random networks
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Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation
A tensor version of Kronecker-graph denoising and parameter inference is presented, but it reuses prior matrix results with a layer index and contains model-definition and proof gaps.