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Hypergraph Learning.pdf

learning-over-families-of-sets-hypergraph-representation-learning-for-higher-order-tasks.pdf
Preview of Hypergraph Learning
🔗 Source: assets.amazon.science
📊 Size: 587 KB
📄 Pages: 11 pages
⬇️ Downloads: 90

Summary

Graph representation learning has limitations in relational domains with non-pairwise interactions, which can be better represented as hyperedges in a non-uniform hypergraph. Existing hypergraph neural networks are limited in their applicability to non-uniform hypergraphs. This work proposes a hypergraph neural network that exploits the incidence structure to learn expressive representations of variable-sized hyperedges, preserving local-isomorphism and permutation invariance. The model is applied to hyperedge classification and expansion tasks, demonstrating significant improvement in accuracy over state-of-the-art models on multiple real-world hypergraph datasets.

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Description

Graph representation learning has limitations in relational domains with non-pairwise interactions, which can be better represented as hyperedges in a...

Technical Information

  • File Format: PDF
  • File Size: 587 KB
  • Pages: 11
  • Language: EN
  • Total Downloads: 90
  • Last Updated: 2 weeks ago

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This PDF document about Hypergraph Learning provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Hypergraph Learning.

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