arXiv (All)
open access
PreGS: A Parameter-Transfer-Based Multi-Expert Graph Neural Network for Node Classification
Graph neural networks have achieved strong performance in node classification by aggregating information from graph neighborhoods. However, a single aggregation mechanism may be insufficient to capture diverse structural patterns across graph datasets. Moreover, independently training multiple struc
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
- Plain Transformers are Surprisingly Powerful Link Predictors#1118471
- Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs#998882
- Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization#363267
- Unifying Graph Neural Networks Through a Common Layer Equation#620512
- Unifying Graph Neural Networks Through a Common Layer Equation#618833
Record · ID 1062207
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