arXiv (All)
open access
HyperFuse: Fast Self-Supervised Node Embeddings for Attributed Hypergraphs
Self-supervised hypergraph representation learning can produce informative node embeddings, but existing methods often require deep encoders trained for hundreds of epochs, making embedding generation costly even for hypergraphs with a few thousand nodes. This limits applications requiring embedding
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Interpretable Hypergraph Learning via Neural Additive Models#1216833
- Self-Supervised Pretraining of Molecular Graph Encoders with LeJEPA#927561
- Advancing graph neural networks : addressing representation challenges in the vision domain#413324
- Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding#1217546
- Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation#1049613
Record · ID 1187889
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive