arXiv (All)
open access
Learning Propagation Geometry from Message-Passing Feedback
Learning local geometry enables graph neural networks (GNNs) to adapt how they compare and integrate neighborhood information. However, estimating geometry from aggregated representations can overlook variation among individual messages and dependencies across feature dimensions. We propose GeoF, a
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
- Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation#1049613
- Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs#998882
- Learning Ad Hoc Network Dynamics via Graph-Structured World Models#18974
- What Do Students Learn? A Feature-Level Analysis of Dark Knowledge#820494
- Towards Trustworthy, Efficient, and Scalable Machine Learning Systems for Graphs and Foundation Models#996300
Record · ID 1128970
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