Saved in:
| Main Authors: | Lu, Shengyao, Mills, Keith G., He, Jiao, Liu, Bang, Niu, Di |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.14578 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
by: Lu, Shengyao, et al.
Published: (2024)
by: Lu, Shengyao, et al.
Published: (2024)
Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction
by: Lu, Shengyao, et al.
Published: (2025)
by: Lu, Shengyao, et al.
Published: (2025)
Building Optimal Neural Architectures using Interpretable Knowledge
by: Mills, Keith G., et al.
Published: (2024)
by: Mills, Keith G., et al.
Published: (2024)
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
by: Chen, Zhuomin, et al.
Published: (2024)
by: Chen, Zhuomin, et al.
Published: (2024)
Graph-based Integrated Gradients for Explaining Graph Neural Networks
by: Simpson, Lachlan, et al.
Published: (2025)
by: Simpson, Lachlan, et al.
Published: (2025)
GraphXAIN: Narratives to Explain Graph Neural Networks
by: Cedro, Mateusz, et al.
Published: (2024)
by: Cedro, Mateusz, et al.
Published: (2024)
Explaining Graph Neural Networks for Node Similarity on Graphs
by: Daza, Daniel, et al.
Published: (2024)
by: Daza, Daniel, et al.
Published: (2024)
Applying Graph Explanation to Operator Fusion
by: Mills, Keith G., et al.
Published: (2024)
by: Mills, Keith G., et al.
Published: (2024)
Explaining Graph Neural Networks via Structure-aware Interaction Index
by: Bui, Ngoc, et al.
Published: (2024)
by: Bui, Ngoc, et al.
Published: (2024)
Graph Variate Neural Networks
by: Roy, Om, et al.
Published: (2025)
by: Roy, Om, et al.
Published: (2025)
Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
by: Rauchwerger, Levi, et al.
Published: (2024)
by: Rauchwerger, Levi, et al.
Published: (2024)
Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes
by: Wang, Xuemin, et al.
Published: (2024)
by: Wang, Xuemin, et al.
Published: (2024)
Relevant Walk Search for Explaining Graph Neural Networks
by: Xiong, Ping, et al.
Published: (2026)
by: Xiong, Ping, et al.
Published: (2026)
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
by: Baghershahi, Peyman, et al.
Published: (2025)
by: Baghershahi, Peyman, et al.
Published: (2025)
GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
by: Sahoo, Rishi Raj, et al.
Published: (2026)
by: Sahoo, Rishi Raj, et al.
Published: (2026)
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
by: Zhang, He, et al.
Published: (2022)
by: Zhang, He, et al.
Published: (2022)
Enforcing convex constraints in Graph Neural Networks
by: Rashwan, Ahmed, et al.
Published: (2025)
by: Rashwan, Ahmed, et al.
Published: (2025)
BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
by: Sloneker, Whitney, et al.
Published: (2024)
by: Sloneker, Whitney, et al.
Published: (2024)
LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
by: Geng, Chuqin, et al.
Published: (2025)
by: Geng, Chuqin, et al.
Published: (2025)
L2XGNN: Learning to Explain Graph Neural Networks
by: Serra, Giuseppe, et al.
Published: (2022)
by: Serra, Giuseppe, et al.
Published: (2022)
Explaining the Explainers in Graph Neural Networks: a Comparative Study
by: Longa, Antonio, et al.
Published: (2022)
by: Longa, Antonio, et al.
Published: (2022)
Soft-Evidence Fused Graph Neural Network for Cancer Driver Gene Identification across Multi-View Biological Graphs
by: Chen, Bang, et al.
Published: (2025)
by: Chen, Bang, et al.
Published: (2025)
AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
by: Das, Siddhartha Shankar, et al.
Published: (2024)
by: Das, Siddhartha Shankar, et al.
Published: (2024)
On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
by: Rezaei, Razieh, et al.
Published: (2024)
by: Rezaei, Razieh, et al.
Published: (2024)
Graph Neural Networks for Graphs with Heterophily: A Survey
by: Zheng, Xin, et al.
Published: (2022)
by: Zheng, Xin, et al.
Published: (2022)
Attacks on Node Attributes in Graph Neural Networks
by: Xu, Ying, et al.
Published: (2024)
by: Xu, Ying, et al.
Published: (2024)
DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations
by: Wang, Jinlu, et al.
Published: (2024)
by: Wang, Jinlu, et al.
Published: (2024)
Faithful Interpretation for Graph Neural Networks
by: Hu, Lijie, et al.
Published: (2024)
by: Hu, Lijie, et al.
Published: (2024)
Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learning
by: Wang, Ziyan, et al.
Published: (2024)
by: Wang, Ziyan, et al.
Published: (2024)
Qua$^2$SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models
by: Mills, Keith G., et al.
Published: (2024)
by: Mills, Keith G., et al.
Published: (2024)
From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks
by: Lymperopoulos, Thodoris, et al.
Published: (2026)
by: Lymperopoulos, Thodoris, et al.
Published: (2026)
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
by: Li, Binxiong, et al.
Published: (2025)
by: Li, Binxiong, et al.
Published: (2025)
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
by: He, Dongxiao, et al.
Published: (2026)
by: He, Dongxiao, et al.
Published: (2026)
Neighborhood Sampling Does Not Learn the Same Graph Neural Network
by: Niu, Zehao, et al.
Published: (2025)
by: Niu, Zehao, et al.
Published: (2025)
Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention
by: Koistinen, Kosti, et al.
Published: (2026)
by: Koistinen, Kosti, et al.
Published: (2026)
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
by: Xu, Paiheng, et al.
Published: (2023)
by: Xu, Paiheng, et al.
Published: (2023)
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
by: He, Yinhan, et al.
Published: (2024)
by: He, Yinhan, et al.
Published: (2024)
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
by: Ding, Mucong, et al.
Published: (2024)
by: Ding, Mucong, et al.
Published: (2024)
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization
by: Liu, Ganlin, et al.
Published: (2024)
by: Liu, Ganlin, et al.
Published: (2024)
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
by: He, Yuntian, et al.
Published: (2024)
by: He, Yuntian, et al.
Published: (2024)
Similar Items
-
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
by: Lu, Shengyao, et al.
Published: (2024) -
Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction
by: Lu, Shengyao, et al.
Published: (2025) -
Building Optimal Neural Architectures using Interpretable Knowledge
by: Mills, Keith G., et al.
Published: (2024) -
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
by: Chen, Zhuomin, et al.
Published: (2024) -
Graph-based Integrated Gradients for Explaining Graph Neural Networks
by: Simpson, Lachlan, et al.
Published: (2025)