HyDRA is a co-clustering based greedy method for lossless weighted hypergraph summarization that reduces storage 80-93% while supporting direct approximate queries on the summary hypergraph.
R., Abebe, R., Schaub, M
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
An information-theoretic multiplex hypergraph model quantifies synergistic and redundant higher-order interactions in eating disorder psychometric networks, identifying a stable transdiagnostic core and diagnosis-specific combinations.
A convergent GMRF approximation to Whittle-Matern fields on boundaryless Riemannian manifolds using DEC on well-centered simplicial complexes that is agnostic to alpha and kappa.
citing papers explorer
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HyDRA: Lossless Hypergraph Summarization via Co-Clustering
HyDRA is a co-clustering based greedy method for lossless weighted hypergraph summarization that reduces storage 80-93% while supporting direct approximate queries on the summary hypergraph.
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Multiplex Hypergraph Modeling of Higher Order Structures in Psychometric Networks
An information-theoretic multiplex hypergraph model quantifies synergistic and redundant higher-order interactions in eating disorder psychometric networks, identifying a stable transdiagnostic core and diagnosis-specific combinations.
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Approximating Gaussian Whittle-Matern Fields over Well-Centered Triangulations of Riemannian Manifolds
A convergent GMRF approximation to Whittle-Matern fields on boundaryless Riemannian manifolds using DEC on well-centered simplicial complexes that is agnostic to alpha and kappa.