mlsanityData to 7 Oct 2026

ScoreCAM GNN: une explication optimale des r'eseaux profonds sur graphes

arXiv 2207.12748 ↗First posted 26 Jul 2022cs.LGcited by 0

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Abstract (arXiv)

The explainability of deep networks is becoming a central issue in the deep learning community. It is the same for learning on graphs, a data structure present in many real world problems. In this paper, we propose a method that is more optimal, lighter, consistent and better exploits the topology of the evaluated graph than the state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 14 references that are in the map. First posted on arXiv 26 Jul 2022.

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12 of 14 references.

References · 12

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  4. 2018Sanity Checks for Saliency Mapscited by 1,149
  5. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  6. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
  7. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  8. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  9. 2019On the (In)fidelity and Sensitivity for Explanationscited by 309
  10. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  11. 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
  12. 2021GraphSVX: Shapley Value Explanations for Graph Neural Networkscited by 63

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