HISR uses hypergraphs to capture complex semantic relations among entities, mapping them to dedicated subspaces for up to 36.6% better implicit semantic interpretation accuracy than graph-based benchmarks.
Preventing over-smoothing for hypergraph neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
HND models hypergraph feature propagation as an anisotropic diffusion process governed by a continuous-time PDE, discretized into stable neural layers with energy dissipation and boundedness guarantees.
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Implicit Semantic-Aware Communication Based on Hypergraph Reasoning
HISR uses hypergraphs to capture complex semantic relations among entities, mapping them to dedicated subspaces for up to 36.6% better implicit semantic interpretation accuracy than graph-based benchmarks.
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Hypergraph Neural Diffusion: A PDE-Inspired Framework for Hypergraph Message Passing
HND models hypergraph feature propagation as an anisotropic diffusion process governed by a continuous-time PDE, discretized into stable neural layers with energy dissipation and boundedness guarantees.