Global Concept Explanations for Graphs by Contrastive Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Teufel, Jonas, Friederich, Pascal |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
por: Teufel, Jonas, et al.
Publicado: (2023)
por: Teufel, Jonas, et al.
Publicado: (2023)
MEGAN: Multi-Explanation Graph Attention Network
por: Teufel, Jonas, et al.
Publicado: (2022)
por: Teufel, Jonas, et al.
Publicado: (2022)
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
por: Teufel, Jonas, et al.
Publicado: (2025)
por: Teufel, Jonas, et al.
Publicado: (2025)
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
por: Schopmans, Henrik, et al.
Publicado: (2024)
por: Schopmans, Henrik, et al.
Publicado: (2024)
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
por: Canizales, Ronaldo, et al.
Publicado: (2026)
por: Canizales, Ronaldo, et al.
Publicado: (2026)
Hyper-Dimensional Fingerprints as Molecular Representations
por: Teufel, Jonas, et al.
Publicado: (2026)
por: Teufel, Jonas, et al.
Publicado: (2026)
Learning Concept Bottleneck Models from Mechanistic Explanations
por: De Santis, Antonio, et al.
Publicado: (2026)
por: De Santis, Antonio, et al.
Publicado: (2026)
Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning
por: Zuo, Rui, et al.
Publicado: (2024)
por: Zuo, Rui, et al.
Publicado: (2024)
Contrastive Learning and Abstract Concepts: The Case of Natural Numbers
por: Nissani, Daniel N.
Publicado: (2024)
por: Nissani, Daniel N.
Publicado: (2024)
Contrastive Token-level Explanations for Graph-based Rumour Detection
por: Chin, Daniel Wai Kit, et al.
Publicado: (2025)
por: Chin, Daniel Wai Kit, et al.
Publicado: (2025)
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
por: Deuschel, Jannik, et al.
Publicado: (2023)
por: Deuschel, Jannik, et al.
Publicado: (2023)
Variational Graph Contrastive Learning
por: Xie, Shifeng, et al.
Publicado: (2024)
por: Xie, Shifeng, et al.
Publicado: (2024)
ConceptFlow: Hierarchical and Fine-grained Concept-Based Explanation for Convolutional Neural Networks
por: Mu, Xinyu, et al.
Publicado: (2025)
por: Mu, Xinyu, et al.
Publicado: (2025)
ConceptLens: from Pixels to Understanding
por: Dalal, Abhilekha, et al.
Publicado: (2024)
por: Dalal, Abhilekha, et al.
Publicado: (2024)
Interpreting Language Reward Models via Contrastive Explanations
por: Jiang, Junqi, et al.
Publicado: (2024)
por: Jiang, Junqi, et al.
Publicado: (2024)
TACENR: Task-Agnostic Contrastive Explanations for Node Representations
por: Papanikou, Vasiliki, et al.
Publicado: (2026)
por: Papanikou, Vasiliki, et al.
Publicado: (2026)
CLDG: Contrastive Learning on Dynamic Graphs
por: Xu, Yiming, et al.
Publicado: (2024)
por: Xu, Yiming, et al.
Publicado: (2024)
Self-Reinforced Graph Contrastive Learning
por: Hsieh, Chou-Ying, et al.
Publicado: (2025)
por: Hsieh, Chou-Ying, et al.
Publicado: (2025)
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
por: Zhang, Jiaxing, et al.
Publicado: (2025)
por: Zhang, Jiaxing, et al.
Publicado: (2025)
Graph Contrastive Learning with Cohesive Subgraph Awareness
por: Wu, Yucheng, et al.
Publicado: (2024)
por: Wu, Yucheng, et al.
Publicado: (2024)
TopoGCL: Topological Graph Contrastive Learning
por: Chen, Yuzhou, et al.
Publicado: (2024)
por: Chen, Yuzhou, et al.
Publicado: (2024)
Oversmoothing: A Nightmare for Graph Contrastive Learning?
por: Li, Jintang, et al.
Publicado: (2023)
por: Li, Jintang, et al.
Publicado: (2023)
Dual-Kernel Graph Community Contrastive Learning
por: Chen, Xiang, et al.
Publicado: (2025)
por: Chen, Xiang, et al.
Publicado: (2025)
Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
por: Landsgesell, Jonas, et al.
Publicado: (2026)
por: Landsgesell, Jonas, et al.
Publicado: (2026)
Brain Network Classification Based on Graph Contrastive Learning and Graph Transformer
por: Zhu, ZhiTeng, et al.
Publicado: (2025)
por: Zhu, ZhiTeng, et al.
Publicado: (2025)
Deep Contrastive Graph Learning with Clustering-Oriented Guidance
por: Chen, Mulin, et al.
Publicado: (2024)
por: Chen, Mulin, et al.
Publicado: (2024)
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning
por: Wu, Junran, et al.
Publicado: (2024)
por: Wu, Junran, et al.
Publicado: (2024)
LocalGCL: Local-aware Contrastive Learning for Graphs
por: Jiang, Haojun, et al.
Publicado: (2024)
por: Jiang, Haojun, et al.
Publicado: (2024)
Tensor-Fused Multi-View Graph Contrastive Learning
por: Wu, Yujia, et al.
Publicado: (2024)
por: Wu, Yujia, et al.
Publicado: (2024)
Dual-perspective Cross Contrastive Learning in Graph Transformers
por: Yao, Zelin, et al.
Publicado: (2024)
por: Yao, Zelin, et al.
Publicado: (2024)
Perfect Alignment May be Poisonous to Graph Contrastive Learning
por: Liu, Jingyu, et al.
Publicado: (2023)
por: Liu, Jingyu, et al.
Publicado: (2023)
EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection
por: Ren, Jing, et al.
Publicado: (2025)
por: Ren, Jing, et al.
Publicado: (2025)
Local-Global Multimodal Contrastive Learning for Molecular Property Prediction
por: Liu, Xiayu, et al.
Publicado: (2026)
por: Liu, Xiayu, et al.
Publicado: (2026)
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation
por: Achtibat, Reduan, et al.
Publicado: (2022)
por: Achtibat, Reduan, et al.
Publicado: (2022)
Estimation of Concept Explanations Should be Uncertainty Aware
por: Piratla, Vihari, et al.
Publicado: (2023)
por: Piratla, Vihari, et al.
Publicado: (2023)
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
por: Zhang, Tianyu, et al.
Publicado: (2021)
por: Zhang, Tianyu, et al.
Publicado: (2021)
The GECo algorithm for Graph Neural Networks Explanation
por: Calderaro, Salvatore, et al.
Publicado: (2024)
por: Calderaro, Salvatore, et al.
Publicado: (2024)
GLEAMS: Bridging the Gap Between Local and Global Explanations
por: Visani, Giorgio, et al.
Publicado: (2024)
por: Visani, Giorgio, et al.
Publicado: (2024)
Robust Stochastic Graph Generator for Counterfactual Explanations
por: Prado-Romero, Mario Alfonso, et al.
Publicado: (2023)
por: Prado-Romero, Mario Alfonso, et al.
Publicado: (2023)
Regional Explanations: Bridging Local and Global Variable Importance
por: Amoukou, Salim I., et al.
Publicado: (2026)
por: Amoukou, Salim I., et al.
Publicado: (2026)
Ejemplares similares
-
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
por: Teufel, Jonas, et al.
Publicado: (2023) -
MEGAN: Multi-Explanation Graph Attention Network
por: Teufel, Jonas, et al.
Publicado: (2022) -
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
por: Teufel, Jonas, et al.
Publicado: (2025) -
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
por: Schopmans, Henrik, et al.
Publicado: (2024) -
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
por: Canizales, Ronaldo, et al.
Publicado: (2026)