arXiv (All)
open access
XInsight: Revealing Model Insights for GNNs with Flow-based Explanations
Progress in graph neural networks has grown rapidly in recent years, with many new developments in drug discovery, medical diagnosis, and recommender systems. While this progress is significant, many networks are `black boxes' with little understanding of the `what' exactly the network is learning.
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
- An Investigation of Federated GNNs Under Aggregation, Data Poisoning, and Differential Privacy for ICU Length-of-Stay Prediction#1081215
- Detection of Covid-19 based on convolutional neural networks using pre-processed chest X-ray images#104356
- Graph Foundation Models for Recommendation: A Comprehensive Survey#971679
- RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine#1034172
- NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption#630167
Record · ID 925696
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