Saved in:
| Main Authors: | Chen, Yongqiang, Bian, Yatao, Han, Bo, Cheng, James |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.07955 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignment
by: Chen, Yongqiang, et al.
Published: (2024)
by: Chen, Yongqiang, et al.
Published: (2024)
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations
by: Xie, Binghui, et al.
Published: (2024)
by: Xie, Binghui, et al.
Published: (2024)
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
by: Zhu, Yuchang, et al.
Published: (2024)
by: Zhu, Yuchang, et al.
Published: (2024)
Graph Unitary Message Passing
by: Qiu, Haiquan, et al.
Published: (2024)
by: Qiu, Haiquan, et al.
Published: (2024)
Faithful Interpretation for Graph Neural Networks
by: Hu, Lijie, et al.
Published: (2024)
by: Hu, Lijie, et al.
Published: (2024)
The Interpretable and Effective Graph Neural Additive Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024)
by: Bechler-Speicher, Maya, et al.
Published: (2024)
Interpretable Graph Neural Networks for Tabular Data
by: Alkhatib, Amr, et al.
Published: (2023)
by: Alkhatib, Amr, et al.
Published: (2023)
Factor Graph-based Interpretable Neural Networks
by: Li, Yicong, et al.
Published: (2025)
by: Li, Yicong, et al.
Published: (2025)
Interpreting Temporal Graph Neural Networks with Koopman Theory
by: Guerra, Michele, et al.
Published: (2024)
by: Guerra, Michele, et al.
Published: (2024)
Interpretable Graph Neural Networks for Heterogeneous Tabular Data
by: Alkhatib, Amr, et al.
Published: (2024)
by: Alkhatib, Amr, et al.
Published: (2024)
Graph Structure Learning with Interpretable Bayesian Neural Networks
by: Wasserman, Max, et al.
Published: (2024)
by: Wasserman, Max, et al.
Published: (2024)
Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation
by: Guo, Jinjiang, et al.
Published: (2022)
by: Guo, Jinjiang, et al.
Published: (2022)
From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
by: Yang, Jie, et al.
Published: (2025)
by: Yang, Jie, et al.
Published: (2025)
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
by: Chen, Yuanyuan, et al.
Published: (2021)
by: Chen, Yuanyuan, et al.
Published: (2021)
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
by: Wang, Yuwen, et al.
Published: (2024)
by: Wang, Yuwen, et al.
Published: (2024)
MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification
by: Yang, Tiantian, et al.
Published: (2025)
by: Yang, Tiantian, et al.
Published: (2025)
GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks
by: Yue, Xiao, et al.
Published: (2025)
by: Yue, Xiao, et al.
Published: (2025)
A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
by: Tremblay, Nicolas, et al.
Published: (2026)
by: Tremblay, Nicolas, et al.
Published: (2026)
On the Interpretability of Quantum Neural Networks
by: Pira, Lirandë, et al.
Published: (2023)
by: Pira, Lirandë, et al.
Published: (2023)
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks
by: Anakok, Emre, et al.
Published: (2025)
by: Anakok, Emre, et al.
Published: (2025)
Towards Scalable Oversight with Collaborative Multi-Agent Debate in Error Detection
by: Chen, Yongqiang, et al.
Published: (2025)
by: Chen, Yongqiang, et al.
Published: (2025)
FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
by: Raut, Riddhiman, et al.
Published: (2025)
by: Raut, Riddhiman, et al.
Published: (2025)
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
by: Lukyanov, Kirill, et al.
Published: (2025)
by: Lukyanov, Kirill, et al.
Published: (2025)
SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs
by: Fang, Lanting, et al.
Published: (2024)
by: Fang, Lanting, et al.
Published: (2024)
CONFINE: Conformal Prediction for Interpretable Neural Networks
by: Huang, Linhui, et al.
Published: (2024)
by: Huang, Linhui, et al.
Published: (2024)
Interpreting Deep Neural Networks with the Package innsight
by: Koenen, Niklas, et al.
Published: (2023)
by: Koenen, Niklas, et al.
Published: (2023)
Learning to Learn with Contrastive Meta-Objective
by: Wu, Shiguang, et al.
Published: (2024)
by: Wu, Shiguang, et al.
Published: (2024)
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
by: Rao, Jiahua, et al.
Published: (2024)
by: Rao, Jiahua, et al.
Published: (2024)
Fragment-Wise Interpretability in Graph Neural Networks via Molecule Decomposition and Contribution Analysis
by: Musiał, Sebastian, et al.
Published: (2025)
by: Musiał, Sebastian, et al.
Published: (2025)
Massive Activations in Graph Neural Networks: Decoding Attention for Domain-Dependent Interpretability
by: Bini, Lorenzo, et al.
Published: (2024)
by: Bini, Lorenzo, et al.
Published: (2024)
Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers
by: Chen, Xuexin, et al.
Published: (2025)
by: Chen, Xuexin, et al.
Published: (2025)
NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation
by: Zhuang, Yuanxin, et al.
Published: (2025)
by: Zhuang, Yuanxin, et al.
Published: (2025)
Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring
by: Wang, Fang, et al.
Published: (2025)
by: Wang, Fang, et al.
Published: (2025)
Seeking Interpretability and Explainability in Binary Activated Neural Networks
by: Leblanc, Benjamin, et al.
Published: (2022)
by: Leblanc, Benjamin, et al.
Published: (2022)
Towards Interpretable Deep Neural Networks for Tabular Data
by: Elhadri, Khawla, et al.
Published: (2025)
by: Elhadri, Khawla, et al.
Published: (2025)
Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
by: Lucena, Anaísa, et al.
Published: (2026)
by: Lucena, Anaísa, et al.
Published: (2026)
GPEX, A Framework For Interpreting Artificial Neural Networks
by: Akbarnejad, Amir, et al.
Published: (2021)
by: Akbarnejad, Amir, et al.
Published: (2021)
Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks
by: Wang, Song, et al.
Published: (2025)
by: Wang, Song, et al.
Published: (2025)
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis
by: Zheng, Kaizhong, et al.
Published: (2023)
by: Zheng, Kaizhong, et al.
Published: (2023)
Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression
by: Hu, Jiao, et al.
Published: (2024)
by: Hu, Jiao, et al.
Published: (2024)
Similar Items
-
HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignment
by: Chen, Yongqiang, et al.
Published: (2024) -
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations
by: Xie, Binghui, et al.
Published: (2024) -
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
by: Zhu, Yuchang, et al.
Published: (2024) -
Graph Unitary Message Passing
by: Qiu, Haiquan, et al.
Published: (2024) -
Faithful Interpretation for Graph Neural Networks
by: Hu, Lijie, et al.
Published: (2024)