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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.NA 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
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.