arXiv (All)
open access
Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark
Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined ground truth, such as a motif planted in the data. This protocol implicitly assumes that a GNN trained on such data relies on the intended motif. Although
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
- Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark#1181645
- RACE: Relation-Level Counterfactual Explanations for Heterogeneous Graph Neural Networks#1141963
- Reachability is not enough: Diagnosing long-range behavior in GNNs#1118747
- How Much Does Message Passing Matter? A Drop-In Study of GNN Layers for Neural Network Graph Regression#1249423
- Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation#1049613
Record · ID 1188204
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