Hypergraph neural networks obey a strict expressivity hierarchy indexed by hypertree width, creating a Width Wall that no fixed-depth model, hidden dimension, or training procedure can cross for wider patterns.
You are AllSet: A multiset function framework 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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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HGPM learns compositional patterns in hypergraphs by subset tokenization and inclusion-aware masked Transformer reconstruction, matching or exceeding SOTA on ten benchmarks and correctly identifying inhibitory drug additions in adverse-event prediction where prior methods fail.
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The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks
Hypergraph neural networks obey a strict expressivity hierarchy indexed by hypertree width, creating a Width Wall that no fixed-depth model, hidden dimension, or training procedure can cross for wider patterns.
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Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions
HGPM learns compositional patterns in hypergraphs by subset tokenization and inclusion-aware masked Transformer reconstruction, matching or exceeding SOTA on ten benchmarks and correctly identifying inhibitory drug additions in adverse-event prediction where prior methods fail.