awesome-graph-explainability-papers

Papers about the explainability of GNNs

Surveys

1. [ACM computing survey 25] Explaining the Explainers in Graph Neural Networks: a Comparative Study paper
2. [Proceedings of the IEEE 24] Trustworthy Graph Neural Networks: Aspects, Methods and Trends paper
3. [Preprint 24] Graph-Based Explainable AI: A Comprehensive Survey paper
4. [Arixv 23] A Survey on Explainability of Graph Neural Networks paper
5. [ACM computing survey] A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges paper
6. [TPAMI 22]Explainability in graph neural networks: A taxonomic survey. Yuan Hao, Yu Haiyang, Gui Shurui, Ji Shuiwang. paper
7. [Arxiv 22]A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics paper
8. [Arxiv 22] A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection paper
9. [Big Data 2022]A Survey of Explainable Graph Neural Networks for Cyber Malware Analysis paper
10. [Machine Intelligence Research 24] A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitypaper
11. [Book 23] Generative Explanation for Graph Neural Network: Methods and Evaluation paper

Platforms

1. PyTorch Geometric [Document] [Blog]
2. DIG: A Turnkey Library for Diving into Graph Deep Learning Research paper Code
3. GraphXAI: Evaluating Explainability for Graph Neural Networks paper Code
4. GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks paper Code
5. GNNExplainer and PGExplainer paper Code
6. BAGEL: A Benchmark for Assessing Graph Neural Network Explanations [paper]Code

Year 2026

• [TPAMI 26]Out-of-Distribution-Resistant Evaluations for Explanations of Graph Neural Networks[paper]
• [TPAMI 26] Addressing Structural Distribution Shift in Explanations for Graph Neural Networks[paper]
• [TPAMI 26]A Novel Approach to GNN Explainability: Distilling Knowledge With Inter-Layer Alignment[paper]
• [ICLR 26] GNN Explanations that do not Explain and How to find Them[paper]
• [ICLR 26] Self-Consistency Improves the Trustworthiness of Self-Interpretable GNNs [paper]
• [SIGKDD 26] Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models. [paper]
• [WWW 26]Explainable Graph Sparsification with Shapley Values[paper]
• [WWW 26]SGExplainer: Balanced Path-based Signed Graph Neural Network Explanation for Link Sign Prediction[paper]
• [WWW 26]Identification of Influential Node Group in Attributed Graph through Explaining Graph Neural Network[paper]
• [WWW 26] Discrete Diffusion-Based Model-Level Explanation of Heterogeneous GNNs with Node Features [paper]
• [WWW 26]Explaining Synergistic Effects in Social Recommendations[paper]
• [WWW 26] SliceGX: Layer-wise GNN Explanation with Model-slicing [paper]
• [WWW 26]CausalSKyHop: Knowledge-Aware Causal Explanation of Dynamic GNNs via Higher-Order Semantic Reasoning[paper]
• [WWW 26] Robust Heterogeneous Graph Neural Network Explainer with Graph Information Bottleneck [paper]
• [ACL 26] From nodes to narratives: Explaining graph neural networks with llms and graph context [paper]
• [AAAI 26] Generating In-Distribution Counterfactual Explanation for Graph Neural Networks[paper]
• [AAAI 26] Interpretable and Robust Behavior Abstraction via Environment-Disentangled Heterogeneous Graph[paper]
• [AAAI 26] Explaining Temporal Graph Neural Network via Quantum-Inspired Evolutionary Algorithm[paper]
• [AAAI 26] Self-Interpretable Subgraph Neural Network with Deep Reinforcement Walk Exploration[paper]
• [AAAI 26] CastX: Cohort-Level Causal Inference Meets Statistical Testing for Faithful and Reliable GNN Explanations[paper]
• [TIFS 26]Learning Subgraph-Based Normality for Interpretable Graph-Level Anomaly Detection[paper]
• [TMLR 26]AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks[paper]
• [TMLR 26] Explainable Graph Learning for Particle Accelerator Operations [paper]
• [TMLR 26] Explaining Graph Neural Networks for Node Similarity on Graphs[paper]
• [AISTATS 26]Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls[paper]
• [TKDE 26] Learning From Graph-Graph Relationship: A New Perspective on Graph-Level Anomaly Detection [paper]
• [TKDE 26] Contrastive Fidelity-Maximised Explanations for Graph-Based Rumour Detection[paper]
• [Arxiv 26.05] From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning [paper]
• • [Arxiv 26.05] Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction [paper]
• [Arxiv 26.05] A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts] Cognitive Explainers of Graph Neural Networks Based on Medical Concepts [paper]
• [Arxiv 26.05] GRAFT: Auditing Graph Neural Networks via Global Feature Attribution [paper]
• [Arxiv 26.05] Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It[paper]
• [Arxiv 26.05] Watermarking Graph Neural Networks Via Explanations For Ownership Protection[paper]
• [Preprint 26] Explainability in Dynamic Graph Anomaly Detection: X-TADDY[paper]
• [Arxiv 26.05] GRAFT: Auditing Graph Neural Networks via Global Feature Attribution[paper]
• [Arxiv 26.03] ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment[paper]
• [Arxiv 26.03]Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer[paper]
• [ArXiv 26.03] Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks [paper]
• [Arxiv 26.02]Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection[paper]
• [Arxiv 26.02]Is Meta-Path Attention an Explanation? Evidence of Alignment and Decoupling in Heterogeneous GNNs[paper]
• [Arxiv 26.02]Quantifying Explanation Quality in Graph Neural Networks using Out-of-Distribution Generalization[paper]
• [Arxiv 26.01]GCFX: Generative Counterfactual Explanations for Deep Graph Models at the Model Level[paper]
• [Arxiv 26.01]FSX: Message Flow Sensitivity Enhanced Structural Explainer for Graph Neural Networks[paper]
• [Arxiv 26.01]Transparent Malware Detection With Granular Assembly Flow Explainability via Graph Neural Networks[paper]
• [Information Processing & Management 26] PAGSL: Path-Augmenting Graph Structure Learning for explainable scholar recommendation[paper]
• [Scientific Reports 26] Generating explainable hypotheses for drug repurposing with graph neural networks[paper]
• [ICASSP 26]Prototype-Based Information Bottleneck for Explainable Heterogeneous Temporal Graph Neural Networks[paper]
• [XAI 26]TACENR: Task-Agnostic Contrastive Explanations for Node Representations[paper]
• [Expert Systems 26]CausGNN: A Causal-Based Explanation Framework for Graph Neural Networks[paper]
• [Computing 26] GECo: a community-based graph neural network explainer[paper]
• [Neurocomputing 26] GNN-EGG: Graph neural network explanations via graph generation[paper]
• [Journal of Complex Networks 26]Assessing the explainability of Graph Neural Networks in random graphs classification task Get[paper]
• [IUI 26]Optimal Explanations: A Quantitative Model of Human Error in Causal Graph Interpretation[paper]
• [Applied Intelligence]An explainable graph neural network framework for illicit financial transaction detection[paper]
• [SSRN Electronic Journal]NEXUS-IDS: From Structural to Semantic Explainability in Flow-Node Graph Neural Networks for IoT Intrusion Detection[paper]
• [Preprint 26]GNN Explainers 2.0: A Paradigm for User-Oriented, Data-Guided Explanations[paper]
• [ENGINEERING Chemical Engineering]AdapGNN: enhancing the explainability of GNN models in molecular properties prediction[paper]
• [Information Science 26]Post-hoc explainability of graph neural networks: A comprehensive survey[paper]
• [IEEE Transactions on Systems, Man, and Cybernetics: Systems]GroupEx: Toward Group-Level Explanations of Graph Neural Networks[paper]
• [Reliability Engineering & System Safety]Temporal causal graph-based attention gated recurrent unit for interpretable fault diagnosis in nuclear power plants[paper]
• [CCNCPS 26]Reliability-aware and Explainability-Driven Evaluation of Graph Neural Networks on Citation Networks[paper]
• [Pattern Recognition 26]Enhancing graph learning interpretability through modulating cluster information flow[paper]
• [BioData Mining 26]Explainable AI-driven graph-based neural networks for mucopolysaccharidoses diagnosis[paper]
• [Data Science and Engineering]Generating Counterfactual Temporal Motifs: Unraveling the Mysteries of Temporal Graph Neural Networks[paper]
• [International Journal of Geo-Information]BD-GNN: Integrating Spatial and Administrative Boundaries in Property Valuation Using Graph Neural Networks[paper]
• [Asia and South Pacific Design Automation Conference 26]GALA: An Explainable GNN-based Approach for Enhancing Oracle-Less Logic Locking Attacks Using Functional and Behavioral Features[paper]
• [IEEE Transactions on Computational Social Systems]Causal Graph Learning for Face-Based Interpretable Hierarchical Diagnosis of Depression[paper]
• [IEEE Transactions on Reliability]KGC-Explainer: Toward Explainable Knowledge Graph Completion[paper]
• [Cybersecurity 26]XGA-E: an explainability-enhanced graph neural network for network traffic anomaly detection[paper]
• [VLSID 26]Hardware Trojan Detection and Interpretation Using Graph Neural Networks[paper]
• [Neurosymbolic AI Journal]Towards Semantic Understanding of Graph Neural Network Layers Embedding with Functional Semantic Activation Mapping[paper]
• [Mach. Learn. Knowl. Extr. 2026]Enhancing GNN Explanations for Malware Detection with Dual Subgraph Matching[paper]
• [Natural Resources Research]GTF: A New Interpretable Graph Neural Network for Geochemical Anomaly Detection in Mineral Prospectivity Mapping[paper]
• [QC+AI 2026]QGSHAP: Quantum Acceleration for Faithful GNN Explanations[paper]
• [DSAA 26] Explainability of Molecular Graph Neural Network [paper]
• [Neural Netw 26] BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop [paper]

Year 2025

1. [NMI 25] Towards unveiling sensitive and decisive patterns in explainable AI with a case study in geometric deep learning[paper]
2. [NeurIPS 25] Robust Explanations of Graph Neural Networks via Graph Curvatures[paper]
3. [NeurIPS 25] GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability[paper]
4. [NeurIPS 25] On Logic-based Self-Explainable Graph Neural Networks [paper]
5. [NeurIPS 25] Interpretable and Parameter Efficient Graph Neural Additive Models with Random Fourier Features [paper]
6. [ICML 25] On Explaining Equivariant Graph Networks via Improved Relevance Propagation [paper]
7. [ICML 25] TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration [paper]
8. [ICML 25] RISE: Radius of influence based subgraph extraction for 3d molecular graph explanation [paper]
9. [ICML 25] Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learning [paper]
10. [ICML 25] CoDy: Counterfactual Explainers for Dynamic Graphs [paper]
11. [ICML 25] Redundancy undermines the trustworthiness of self-interpretable GNNs [paper]
12. [ICML 25] Beyond topological self-explainable GNNs: A formal explainability perspective [paper]
13. [ICLR 25] Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks[paper]
14. [ICLR 25] How do large language models understand graph patterns? a benchmark for graph pattern comprehension [paper]
15. [ICLR 25] From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks[paper]
16. [ICLR 25] Paradigm Shift of GNN Explainer from Label Space to Prototypical Representation Space [paper]
17. [ICLR 25] Training-free Counterfactual Explanation for Temporal Graph Model Inference [paper]
18. [ICLR 25] MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy [paper]
19. [ICLR 25] Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs [paper]
20. [ICLR 25] Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks [paper]
21. [ICLR 25] Towards Explaining the Power of Constant-depth Graph Neural Networks for Linear Programming [paper]
22. [ICLR 25] Explanations of GNN on Evolving Graphs via Axiomatic Layer edges [paper]
23. [ICLR 25] MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation [paper]
24. [KDD 25] Explaining gnn explanations with edge gradients [paper]
25. [KDD 25] Harnessing influence function in explaining graph neural networks [paper]
26. [KDD 25] Global interpretable graph-level anomaly detection via prototype [paper]
27. [KDD 25] Causal-aware graph neural architecture search under distribution shifts [paper]
28. [KDD 25] 3dgraphx: Explaining 3d molecular graph models via incorporating chemical priors [paper]
29. [KDD 25] Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Molecular Graph Learning [paper]
30. [KDD 25] Is your explanation reliable: Confidence-aware explanation on graph neural networks [paper]
31. [VLDB 25] eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules[paper]
32. [ACL 25] GraphNarrator: Generating Textual Explanations for Graph Neural Networks [paper]
33. [AAAI 25] Higher Order Structures For Graph Explanations [paper]
34. [AAAI 25] Self-Explainable Graph Transformer for Link Sign Prediction[paper]
35. [AAAI 25] Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint[paper]
36. [AAAI 25] Graph Segmentation and Contrastive Enhanced Explainer for Graph Neural Networks [paper]
37. [TMLR 25] Hierarchical Language Model Design For Interpretable Graph Reasoning [paper]
38. [TMLR 25] Explaining Node Embeddings [paper]
39. [TMLR 25] Evaluating explainability techniques on discrete-time graph neural networks [paper]
40. [TMLR 25] [RE] GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries [paper]
41. [TIFS 25] Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics[paper]
42. [WWW 25] SEHG: bridging interpretability and prediction in self-explainable heterogeneous graph neural networks [paper]
43. [ICDE 25] Revelio: Revealing Important Message Flows in Graph Neural Networks [paper]
44. [TKDE 25] GAFExplainer: Global view explanation of graph neural networks through attribute augmentation and fusion embedding [paper]
45. [TKDE 25] A Multi-Objective Explanation Framework for Graph Neural Networks [paper]
46. [TOIS 25] SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction [paper]
47. [WSDM 25] Impo: Interpretable memory-based prototypical pooling [paper]
48. [IJCAI 25] Explainable graph neural networks via structural externalities [paper]
49. [IJCAI 25] DGExplainer: Explaining Dynamic Graph Neural Networks via Relevance Back-propagation [paper]
50. [IJCAI 25] Explainable Graph Representation Learning via Graph Pattern Analysis [paper]
51. [IJCAI 25] Towards comprehensive and prerequisite-free explainer for graph neural networks [paper]
52. [ICDM 25] Explanations Go Linear: Post-Hoc Explainability for Tabular Data with Interpretable Meta-Encoding [paper]
53. [CIKM 25] Assessing Natural Language Explanations of Relational Graph Neural Networks [paper]
54. [TNNLS 25] CiRLExplainer: Causality-Inspired Explainer for Graph Neural Networks via Reinforcement Learning [paper]
55. [TKDD 25] DyExplainer: Explainable Dynamic Graph Neural Networks [paper]
56. [TKDD 24] Efficient GNN Explanation via Learning Removal-based Attribution [paper]
57. [TKDD 25] Towards Prototype-Based Self-Explainable Graph Neural Network [paper]
58. [TNNLS 25] Gen-graphex: Generative in-distribution graph explanations for time-efficient model-level interpretability of gnns [paper]
59. [ACM TIST 25] Help Me Screen: Analyzing and Predicting the Success of Start-ups in Dynamic Venture Capital Networks [paper]
60. [ACM TIST 25] Gcfexplainer: Global counterfactual explainer for graph neural networks [paper]
61. [Engineering Applications of Artificial Intelligence] Dual Explanations via Subgraph Matching for Malware Detection [paper]
62. [Information Sciences 25] On the Consistency of GNN Explanations for Malware Detection [paper]
63. [TETCI 25] Interpretable and Adaptive Graph Contrastive Learning With Information Sharing for Biomedical Link Prediction [paper]
64. [ACM Computing Surveys] Can Graph Neural Networks be Adequately Explained? A Survey [paper]
65. [IEEE TNSRE] Finding Neural Biomarkers for Motor Learning and Rehabilitation using an Explainable Graph Neural Network [paper]
66. [Springer FCS] Learning from shortcut: a shortcut-guided approach for explainable graph learning [paper]
67. [NN] Local interpretable spammer detection model with multi-head graph channel attention network [paper]
68. [Applied Intelligence] KnowGNN: a knowledge-aware and structure-sensitive model-level explainer for graph neural networks [paper]
69. [Measurement Science and Technology] Tree structure guided graph neural networks for soft sensing and anomaly regulation[paper]
70. [Data Science for Transportation]Exploring the Potential and Utility of Graph Neural Networks in Crash Analysis and Safety Assessment[paper]
71. [IEEE Access]MOOC Dropout Prediction Using Explainable Relational Graph Convolution[paper]
72. [ICDMW 25]A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation[paper]
73. [Journal of Statistical Theory and Applications 25] BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks[paper]
74. [Machine Learning 25] MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings [paper]
75. [Sci Rep 25] Fraud detection and explanation in medical claims using GNN architectures [paper]
76. [ICCBD+AI 25] Learning Explanations of Graph Neural Networks via Multi-granularity Subgraph Masking [paper]
77. [ITSC 25] An Ante-Hoc Explainable Graph Attention Network for Routing Optimization [paper]
78. [PRICAI 25] Interpretable Brain Network Analysis for Psychiatric Diagnosis Using Fuzzy Logic [paper]
79. [Comput Ind Eng 25] HeteroX: An interpretable Dual-tier Heterogeneous Graph for sustainable investment decisions [paper]
80. [Comput Mater Continua 25] Graph-Based Intrusion Detection with Explainable Edge Classification Learning [paper]
81. [CIoTSC 25] Explainable Book Recommendations via Graph Convolutional Networks [paper]
82. [CyberSciTech 25] CFExplainerTG: Counterfactual Explanation for Temporal Graph Neural Networks in Cybersecurity [paper]
83. [Concurr Comput Pract Exp 25] GTCExplainer: Interpretable Graph Convolutional Networks for Molecular Activity Prediction [paper]
84. [Mach Learn 25] Bridging XAI and spectral analysis to investigate the inductive biases of deep graph networks [paper]
85. [Data Min Knowl Discov 25] Leveraging internal representations of GNNs with Shapley values [paper]
86. [Pattern Recogn. 25] Trace2Vec: Detecting complex multi-step attacks with explainable graph neural network [paper]
87. [J King Saud Univ Comput Inf Sci 25] DHGNN: A dynamic heterogeneous graph neural network for interpretable inventor collaboration prediction [paper]
88. [DSPP 25] An Efficient Explainability Framework for Graph Neural Networks [paper]
89. [ASP-DAC 25] Understand and Detect: Lithographic Hotspot Detection by the Interpretable Graph Attention Network [paper]
90. [CDVE 25] Towards Interpretable GNNs: A Feasibility Study on Subgraph-Level Explanation Classification [paper]
91. [KBS 25] Fair path explanations for heterogeneous link prediction from a community perspective [paper]
92. [KBS 25] Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation[paper]
93. [SMC 25] MIP-Explainer: An Explainability Method for Graph Neural Networks Based on Mutual Information [paper]
94. [Discov Artif Intell 26] GraphAware: Interpretable machine learning on graphs [paper]
95. [Neurocomputing 25] STD-Explain: Generalizing explanations for spatio-temporal graph convolutional networks based on spatio-temporal decoupled perturbation [paper]
96. [TPDS 25] XDGNN: Efficient Distributed GNN Training via Explanation-Guided Subgraph Expansion [paper]
97. [J Softw Evol Process 25] Towards Explainable Code Readability Classification With Graph Neural Networks [paper]
98. [REW 25] Research on Requirement Vulnerability Detection Method Based on Graph Neural Network and Counterfactual Explanation [paper]
99. [Appl Soft Comput 25] A multi-hop Shapley-based framework for graph convolutional network node classification explanation [paper]
100. [PLOS Complex Syst 25] Determining graphlet explanations for machine learning on graphs [paper]
101. [TNSM 25] Explainable GNN-Based Approach to Fault Forecasting in Cloud Service Debugging [paper]
102. [ICETCI 25] Making Sense of Communities: A Survey on XAI Techniques in Graph Community Detection [paper]
103. [Appl Intell 25] EGNet: explainable graph neural network with similarity explanation for medication recommendation [paper]
104. [RIME 25] X-node: Self-explanation is all we need [paper]
105. [Pattern Recognit 26] Explanation-guided backdoor defense for ID and OOD attacks in graph neural networks [paper]
106. [J Signal Process Syst 25] HGExplainer: Toward Interpretable Heterogeneous Graph Neural Networks via Meta-path Perturbation [paper]
107. [ECML PKDD 25] Faithful Explanations for Graph Classification Using Logic [paper]
108. [J Chem Inf Model 25] Explainable graph neural networks in chemistry: Combining attribution and uncertainty quantification [paper]
109. [TCCN 25] Geek-explainer: an efficient interpretation method for graph neural networks in SDN [paper]
110. [Neural Netw 25] Learning model-level explanations of graph neural networks via subgraph order embedding space [paper]
111. [IJCNN 25] Visual Analytics for Explainable AI with Spatio-Temporal Data: A Comparative Study [paper]
112. [IJCNN 25] CRoCExplainer: A Tool to Explain Graph Learning with Improved Connectedness Representation. [paper]
113. [Thesis 25] Towards Explainable AI on Graph Neural Networks: XAIG [paper]
114. [Thesis] Explainable Deep Graph Learning: From Feature Attribution to Relational Reasoning [paper]
115. [Thesis] Explainable GNNs in Biomedicine[paper]
116. [Thesis 25] ConceptGNNExplainer: A Self-Supervised Framework for Concept-Based Explanations of Graph Neural Network Predictions [paper]
117. [Thesis 25] Rules and Distributions for Explainable Machine Learning [paper]
118. [Thesis 25] Understanding Neural Burst Patterns Through Graph Neural Network Explainability in Simulated Neuronal Networks [paper]
119. [Thesis 25] Structural Interpretability for Deep Learning in Quantum Chemistry [paper]
120. [Thesis 25] Fast and Accurate Explanations of Graph Neural Networks [paper]
121. [Thesis 25] Enhancing Drug Repositioning With Interpretable Graph Neural Network Models on Biomedical Knowledge Graphs [paper]
122. [Thesis 25] Graph Neural Networks for Interpretable Biomedical Data Analysis in Genomics and Structural Biology [paper]
123. [Appl Soft Comput 25] Auto-configured explainable graph neural networks for multi-site pollution prediction [paper]
124. [Inf Process Manag 25] Augmented graph information bottleneck with type-aware periodicity heterogeneity for explainable crime prediction [paper]
125. [FAccT 25] C2explainer: Customizable mask-based counterfactual explanation for graph neural networks [paper]
126. [Inf Fusion 25] Explaining spatio-temporal graph convolutional networks with spatio-temporal constraints perturbation for action recognition [paper]
127. [TVCG 25] SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-Based Synthetic Lethal Prediction[paper]
128. [ICC 25] Towards Self-Explainable Information Cascade Popularity Prediction [paper]
129. [Modelling 2025]TE-G-SAGE: Explainable Edge-Aware Graph Neural Networks for Network Intrusion Detection[paper]
130. [ICCNC 25] AMSVGAE-Based Causal Inference for Interpretable Graph Neural Networks [paper]
131. [JCad 25] A Visual Recommendation Analysis System Based on the Interpretable Graph Neural Network [paper]
132. [OpenReview 25] DR-CFGNN: A Completion-Aware Framework for Counterfactual Explainability in Graph Neural Networks [paper]
133. [OpenReview 25] LaCore: Laplacian Cohesive Subgraphs for Graph Representation Learning [paper]
134. [OpenReview 25] Self-Guided Explanation for Graph Neural Networks with Semi-Supervision [paper]
135. [OpenReview 25] Self-explainable Molecular Property Prediction via Multi-view Hypergraph Learning [paper]
136. [OpenReview 25] Interpretable Hypergraph Neural Additive Networks [paper]
137. [OpenReview 25] Interpretable Graph Embeddings: Feature-Level Decomposition for Trustworthy Graph Neural Networks [paper]
138. [OpenReview 25] TAME: A Task-Agnostic Framework for Robust Graph Neural Network Explanations via Structural Mixup [paper]
139. [OpenReview 25] MatchEx: Model-Level GNN Explanations with Multi-Granular Insights [paper]
140. [OpenReview 25] Interactive and Explainable Graph Neural Networks with Uncertainty Awareness and Adaptive Human Feedback [paper]
141. [Arxiv 25.12] Enhancing Explainability of Graph Neural Networks Through Conceptual and Structural Analyses and Their Extensions [paper]
142. [bioRxiv 25.12] A Concept-Driven Disentanglement Framework for Interpretable Graph Neural Networks in Structure-Function Coupling [paper]
143. [Neural Networks] On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach [paper]
144. [Arxiv 25.12] GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method [paper]
145. [Arxiv 25.11] ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining [paper]
146. [Arxiv 25.10] Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features [paper]
147. [Arxiv 25.09] Superposition in Graph Neural Networks [paper]
148. [Arxiv 25.09] SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts [paper]
149. [Arxiv 25.08] ProvX: Generating Counterfactual-Driven Attack Explanations for Provenance-Based Detection [paper]
150. [Arxiv 25.08] NAEx: A Plug-and-Play Framework for Explaining Network Alignment [paper]
151. [Arxiv 25.08] Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection [paper]
152. [Arxiv 25.08] Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies [paper]
153. [Arxiv 25.08] From Binary to Continuous: Stochastic Re-Weighting for Robust Graph Explanation [paper]
154. [Arxiv 25.06] RAW-Explainer: Post-hoc Explanations of Graph Neural Networks on Knowledge Graphs [paper]
155. [Arxiv 25.06] FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models [paper]
156. [Arxiv 25.06] GNNAnatomy: Systematic Generation and Evaluation of Multi-Level Explanations for Graph Neural Networks[paper]
157. [Arxiv 25.06] DistShap: Scalable GNN Explanations with Distributed Shapley Values [paper]
158. [Arxiv 25.05] Understanding the Robustness of Graph Neural Networks against Adversarial Attacks [paper]
159. [Arxiv 25.05] LM^2otifs: An Explainable Framework for Machine-Generated Texts Detection [paper]
160. [Arxiv 25.05] A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring [paper]
161. [Arxiv 25.05] B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data [paper]
162. [Arxiv 25.05] MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration [paper]
163. [ArXiv 25.05] Interpreting Graph Inference with Skyline Explanations [paper]
164. [ICLR] LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks [paper]
165. [ArXiv 25.03] Z-rex: human-interpretable GNN explanations for real estate recommendations [paper]
166. [ArXiv 25.03] Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks [paper]
167. [ArXiv 25.03] Identifying evidence subgraphs for financial risk detection via graph counterfactual and factual reasoning [paper]
168. [Arxiv 25.02] Recent advances in malware detection: Graph learning and explainability [paper]
169. [Arxiv 25.01] Watermarking Graph Neural Networks via Explanations for Ownership Protection [paper]
170. [Arxiv 25.01] Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection [paper]
171. [Openreview 25] SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation[paper]
172. [Preprint 25] Faithful and Diverse Subgraph as Explanation for Large Probabilistic Graphical Models [paper]
173. [Preprint 25] Game theoretic explainers for graph neural networks [paper]
174. [Preprint ]Method for Explaining Regression Prediction Results Using Machine Learning on Temporal Graph Data[paper]
175. [Neural Netw 25] Community-influencing path explanation for link prediction in heterogeneous graph neural network [paper]
176. [Neural Comput Appl 25] Explainability graph neural networks with nearest neighbor estimate interpretations [paper]
177. [DASFAA 25] Meta Relation Assisted Explanatory Model for Heterogeneous Graph Neural Networks [paper]
178. [Neural Comput Appl 25] GraphXAI: a survey of graph neural networks (GNNs) for explainable AI (XAI) [paper]
179. [ACM Trans Inf Syst 25] CaGE: A causality-inspired graph neural network explainer for recommender systems [paper]
180. [Inf Fusion 25] InsGNN: Interpretable spatio-temporal graph neural networks via information bottleneck [paper]
181. [Inf Fusion 25] GL-BKGNN: Graphlet-based Bi-Kernel Interpretable Graph Neural Networks [paper]
182. [WIREs DMKD 25] Exploring Causal Learning Through Graph Neural Networks: An In‐Depth Review [paper]
183. [Big Data Res 25] Explainable malware detection through integrated graph reduction and learning techniques [paper]
184. [Brief Bioinform 25] Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers [paper]
185. [TDSC 25] SIGFinger: A subtle and interactive GNN fingerprinting scheme via spatial structure inference perturbation [paper]
186. [Curr Bioinform 25] A graphlet-based explanation generator for graph neural networks over biological datasets [paper]
187. [Inf Sci 25] Towards self-interpretable review spammer group detection [paper]
188. [RIME 25] X-node: Self-explanation is all we need [paper]
189. [Sci Rep 25] Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging [paper]
190. [Inf Fusion 25] InsGNN: Interpretable spatio-temporal graph neural networks via information bottleneck [paper]
191. [Mach Intell Res 25] Counterfactual learning on graphs: A survey [paper]
192. [Autom Constr 25] Automatic clash avoidance in steel reinforcement design using explainable graph neural networks and rebar embedding learning [paper]
193. [Mach Learn 25] An end-to-end explainability framework for spatio-temporal predictive modeling [paper]
194. [IEEE JBHI 25] ExplainMIX: explaining drug response prediction in directed graph neural networks with multi-omics fusion [paper]
195. [ACM TORS 25] Joint Factual and Counterfactual Explanations for Top-k GNN-based Recommendations [paper]
196. [J Comput Biol 25] Building explainable graph neural network by sparse learning for the drug-protein binding prediction [paper]
197. [J Comput Inf Syst 25] Attributing stealth cyberattacks via Temporal probabilistic graph neural networks [paper]
198. [xAI 25] Explaining vision gnns: A semantic and visual analysis of graph-based image classification [paper]
199. [ICSTW 25] Structural Backdoor Attack on IoT Malware Detectors via Graph Explainability [paper]
200. [IEEE Trans Comput Soc Syst 25] MLFormer: Unleashing Efficiency Without Attention for Multimodal Knowledge Graph Embedding [paper]
201. [PeerJ Comput Sci 25] TurkSentGraphExp: an inherent graph aware explainability framework from pre-trained LLM for Turkish sentiment analysis [paper]
202. [IEEE Access 25] Noise-Robust and Interpretable EEG Classification of Hyperactive Disorders Using Graph Convolutional Network [paper]
203. [Electronics 25] Post Hoc Multi-Granularity Explanation for Multimodal Knowledge Graph Link Prediction [paper]
204. [TCSS 25] Contrastive Token-Level Explanations for Graph-Based Rumor Detection [paper]
205. [EPJ Data Sci 25] A new approach to estimate neighborhood socioeconomic status using supermarket transactions and GNNs [paper]
206. [ICBCB 25] Graph Neural Networks Based Explainability of Drug-Target Interactions [paper]
207. [ICAACE 25] CStaG: A Causality-Inspired Stable Generative Model for the Interpretation of Graph Neural Networks [paper]
208. [Procedia Comput Sci 25] Enhancing Stock Market Prediction with Temporal Graph Neural Networks and Large Language Model-Based Explainability [paper]
209. [CEUR 25] Quantitative Assessment of GNN Counterfactual Explanation Robustness and Reproducibility under Adversarial Influence [paper]
210. [CEUR 25] ActiMine-GNN: Activation Mining and Interpretability in Graph Neural Networks [paper]
211. [ResearchGate 25] GraphX-Forecast: A Unified Survey on the Synergy of GNNs, Uncertainty, and XAI for Multi-Horizon Time Series Forecasting [paper]
212. [Informed Machine Learning 25] On the Interplay of Subset Selection and Informed Graph Neural Networks [paper]

Year 2024

1. [TPAMI 24] Towards Inductive and Efficient Explanations for Graph Neural Networks[paper]
2. [TPAMI 24] PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks [paper]
3. [NeurIPS 24] RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task [paper]
4. [NeurIPS 24] GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules[paper]
5. [ICML 24] Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks[paper]
6. [ICML 24] Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks [paper]
7. [ICML 24] Graph Neural Network Explanations are Fragile [paper]
8. [ICML 24] How Interpretable Are Interpretable Graph Neural Networks? [paper]
9. [ICML 24] Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation[paper]
10. [ICML 24] Explaining Graph Neural Networks via Structure-aware Interaction Index [paper]
11. [ICML 24] EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time [paper]
12. [ICLR 24] GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks [paper]
13. [ICLR 24] GOAt: Explaining Graph Neural Networks via Graph Output Attribution [paper]
14. [ICLR 24] Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks [paper]
15. [ICLR 24] GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking [paper]
16. [ICLR 24] UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models [paper]
17. [TMLR 24] InduCE: Inductive Counterfactual Explanations for Graph Neural Networks [paper]
18. [PLDI 24] PL4XGL: A Programming Language Approach to Explainable Graph Learning[paper]
19. [Usenix Security 24] INSIGHT: Attacking Industry-Adopted Learning Resilient Logic Locking Techniques Using Explainable Graph Neural Network[paper]
20. [SIGMOD 24]View-based Explanations for Graph Neural Networks [paper]
21. [ACM SIGMOD Record] The Road to Explainable Graph Neural Networks [paper]
22. [Thesis UCLA] Explainable Artificial Intelligence for Graph Data[paper]
23. [Thesis UVA] Algorithmic Fairness in Graph Machine Learning: Explanation, Optimization, and Certification[paper]
24. [KDD 24] SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning[paper]
25. [KDD 24] Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck[paper]
26. [KDD 24] Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks[paper]
27. [ICDE 24] Generating Robust Counterfactual Witnesses for Graph Neural Networks [paper]
28. [ICDE 24] SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks[paper]
29. [ICSE 24] Coca: Improving and Explaining Graph Neural Network-Based Vulnerability Detection Systems[paper]
30. [AAAI 24] Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks [paper]
31. [AAAI 24] Stratifed GNN Explanations through Sufficient Expansion[paper]
32. [AAAI 24] Factorized Explainer for Graph Neural Networks[paper]
33. [AAAI 24] Self-Interpretable Graph Learning with Sufficient and Necessary Explanations
34. [AAAI 24] Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder [paper]
35. [AISTATS 24] Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process [paper]
36. [WWW 24] Game-theoretic Counterfactual Explanation for Graph Neural Networks [paper]
37. [WWW 24] EXGC: Bridging Efficiency and Explainability in Graph Condensation[paper]
38. [WWW 24] Adversarial Mask Explainer for Graph Neural Networks [paper]
39. [WWW 24] Globally Interpretable Graph Learning via Distribution Matching[paper]
40. [WWW 24] GNNShap: Scalable and Accurate GNN Explanation using Shapley Values [paper]
41. [IEEE TDSC 24] TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support [paper]
42. [ISSTA 2024] Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation [paper]
43. [TMLR 24] Graphon-Explainer: Generating Model-Level Explanations for Graph Neural Networks using Graphons [paper]
44. [ECCV 24] Graph Neural Network Causal Explanation via Neural Causal Models[paper] (https://openreview.net/forum?id=nD1a6hSLhO)
45. [TNNLS 24] BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck [paper]
46. [TKDE 24] On Regularization for Explaining Graph Neural Networks: An Information Theory Perspective [paper]
47. [TAI 24] Learning Counterfactual Explanation of Graph Neural Networks via Generative Flow Network[paper]
48. [TAI 24] Traffexplainer: A Framework towards GNN-based Interpretable Traffic Prediction [paper]
49. [TMC 24] HGExplainer: Heterogeneous Graph Explainer for IoT Device Identification[paper]
50. [LOG 24] xAI-Drop: Don't Use What You Cannot Explain[paper]
51. [LOG 24] MOSE-GNN: A Motif-Based Self-Explaining Graph Neural Network for Molecular Property Prediction [[paper]]
52. [IEEE TMI 24] Multi-Modal Diagnosis of Alzheimer’s Disease using Interpretable Graph Convolutional Networks[paper]
53. [IEEE IoT 24] EXVul: Toward Effective and Explainable Vulnerability Detection for IoT Devices[paper]
54. [IEEE Transactions on Fuzzy Systems] Towards Embedding Ambiguity-Sensitive Graph Neural Network Explainability [paper]
55. [IEEE JBHI] Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response[paper]
56. [IDEAL 2024] Causal Explanation of Graph Neural Networks[paper]
57. [BIBM 24] Seizure Onset Zone Localization Method based on GNN Explanation [paper]
58. [BIBM 24] DDTExplainer: Mining Drug-Disease Therapeutic Mechanisms based on GNN Explainability [paper]
59. [CIKM 24] EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs[paper]
60. [ECML PKDD 24] Towards Few-shot Self-explaining Graph Neural Networks[paper]
61. [ECML PKDD 24] BAGEL: A Benchmark for Assessing Graph Neural Network Explanations [paper]
62. [SDM 24] XGExplainer: Robust Evaluation-based Explanation for Graph Neural Networks[paper]
63. [DASFAA 24] Multi-objective Graph Neural Network Explanatory Model with Local and Global Information Preservation[paper]
64. [DMKD 24] On GNN explanability with activation rules[paper]
65. [KBS 24] Shapley-based graph explanation in embedding space[paper]
66. [KBS 24] GEAR: Learning graph neural network explainer via adjusting gradients[paper]
67. [IEEE TNSM 24] Ensemble Graph Attention Networks for Cellular Network Analytics: From Model Creation to Explainability[paper]
68. [IEEE TNSE 24] GAXG: A Global and Self-adaptive Optimal Graph Topology Generation Framework for Explaining Graph Neural Networks[paper]
69. [IEEE TETCI 24] GF-LRP: A Method for Explaining Predictions Made by Variational Graph Auto-Encoders[paper]
70. [AAAI workshop] Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease[paper]
71. [xAI 24] Global Concept Explanations for Graphs by Contrastive Learning [paper]
72. [Arxiv 24.12] GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs [paper]
73. [Arxiv 24.11] Rethinking Node Representation Interpretation through Relation Coherence[paper]
74. [Preprint 24.11] Chiseling the Graph: An Edge-Sculpting Method for Explaining Graph Neural Networks [paper]
75. [Arxiv 24.10] Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts[paper]
76. [Arxiv 24.10] Explainable Graph Neural Networks Under Fire [paper]
77. [Arxiv 24.09] PAGE: Parametric Generative Explainer for Graph Neural Network [paper]
78. [Arxiv 24.07] CIDER: Counterfactual-Invariant Diffusion-based GNN Explainer for Causal Subgraph Inference[paper]
79. [Arxiv 24.07] LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation[paper]
80. [Arxiv 24.05] SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs[paper]
81. [Arxiv 24.06] Robust Ante-hoc Graph Explainer using Bilevel Optimization [paper]
82. [Arxiv 24.05] Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks [paper]
83. [Arxiv 24.05] Evaluating Neighbor Explainability for Graph Neural Networks [paper]
84. [Arxiv 24.04] Improving the interpretability of GNN predictions through conformal-based graph sparsification [paper]
85. [Arxiv 24.03] Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation[paper]
86. [Arixv 24.03] Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations[paper]
87. [Arxiv 24.02] PAC Learnability under Explanation-Preserving Graph Perturbations[paper]
88. [Arxiv 24.02] Explainable Global Wildfire Prediction Models using Graph Neural Networks[paper]
89. [Arxiv 24.01] On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions[paper]
90. [Openreview 24] Robust Graph Attention for Graph Adversarial Attacks: An Information Bottleneck Inspired Approach[paper]
91. [Openreview 24] Graph Distributional Analytics: Enhancing GNN Explainability through Scalable Embedding and Distribution Analysis[paper]
92. [Mach. Learn. Knowl. Extr. 24] Reliable and Faithful Generative Explainers for Graph Neural Networks[paper]
93. [Machine Learning 24] L2XGNN: Learning to Explain Graph Neural Networks [paper]
94. [Cell Reports Physical Science 24] Superior Polymeric Gas Separation Membrane Designed by Explainable Graph Machine Learning [paper]
95. [ASP=DAC 24] LIPSTICK: Corruptibility-Aware and Explainable Graph Neural Network-based Oracle-Less Attack on Logic Locking[paper]
96. [Biorxiv 24] Community-aware explanations in knowledge graphs with XP-GNN[paper]
97. [ISCV 24] Adaptive Subgraph Feature Extraction for Explainable Multi-Modal Learning[paper]
98. [IJCNN 24] Explanations for Graph Neural Networks using A Game-theoretic Value[paper]
99. [AIxIA 2024] Relating Explanations with the Inductive Biases of Deep Graph Networks [paper]
100. [Neurocomputing] GeoExplainer: Interpreting Graph Convolutional Networks with geometric masking[paper]
101. [Technologies] Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices[paper]
102. [Reliab. Eng. Syst. Saf.] Causal intervention graph neural network for fault diagnosis of complex industrial processes[paper]
103. [Frontiers in big data] Global explanation supervision for Graph Neural Networks[paper]
104. [Information and Software Technology] Graph-based explainable vulnerability prediction[paper]
105. [Information Systems] Heterogeneous graph neural networks for fraud detection and explanation in supply chain finance[paper]
106. [Information Procs. & Mana.] Towards explaining graph neural networks via preserving prediction ranking and structural dependency[paper]
107. [Applied Energy] Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production [paper]
108. [PAKDD 24] Random Mask Perturbation Based Explainable Method of Graph Neural Networks [paper]
109. [Computational Materials Science] Graph isomorphism network for materials property prediction along with explainability analysis[paper]
110. [NN 24] Explanatory subgraph attacks against Graph Neural Networks[paper]
111. [NN 24] GRAM: An interpretable approach for graph anomaly detection using gradient attention maps[paper]
112. [Neural Networks 24] CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis [paper]
113. [NeuroImage 24] BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping[paper]
114. [PAKDD 24] Toward Interpretable Graph Classification via Concept-Focused Structural Correspondence [paper]
115. [ICSE Poster 24] Graph Neural Network based Log Anomaly Detection and Explanation [paper]
116. [ICPR 24] Interpretable Deep Graph-Level Clustering: A Prototype-Based Approach [paper]
117. [AIMA 24] An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer’s Disease Using UK Biobank[paper]
118. [IEEE Transactions] IEEE Transactions on Computational Social Systems[paper]
119. [Journal of Physics] Explainer on GNN-based segmentation networks[paper]
120. [Energy and AI] Electricity demand forecasting at distribution and household levels using explainable causal graph neural network [paper]
121. [HI-AI@KDD 24] Interpretable Graph Model with Prototype-Based Graph Information Bottleneck [paper]
122. [Neurosymbolic Artificial Intelligence] Towards Semantic Understanding of GNN Layers embedding with Functional-Semantic Activation Mapping [paper]
123. [NeSy 2024] Towards Understanding Graph Neural Networks: Functional-Semantic Activation Mapping[paper]
124. [Thesis 24] Explainable and physics-guided graph deep learning for air pollution modelling [paper]
125. [Thesis 24] Influence of molecular structures on graph neural network explainers’ performance[paper]
126. [CEUR 24] Exposing Inductive Biases of Deep Graph Networks through Explainable AI [paper]

Year 2023

1. [NeurIPS 23] Interpretable Graph Networks Formulate Universal Algebra Conjectures[paper]
2. [NeurIPS 23] SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanation [paper]
3. [NeurIPS 23] Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks[paper]
4. [NeurIPS 23] D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion [paper]
5. [NeurIPS 23] TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery [paper]
6. [NeurIPS 23] V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs [paper]
7. [NeurIPS 23] Towards Self-Interpretable Graph-Level Anomaly Detection [paper]
8. [NeurIPS 23] Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis [paper]
9. [NeurIPS 23] Interpretable Prototype-based Graph Information Bottleneck [paper]
10. [ICML 23] Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching [paper]
11. [ICML 23] Relevant Walk Search for Explaining Graph Neural Networks [paper]
12. [ICML 23] Towards Understanding the Generalization of Graph Neural Networks [paper]
13. [ICLR 23] GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks [paper]
14. [ICLR 23] Global Explainability of GNNs via Logic Combination of Learned Concepts [paper]
15. [ICLR 23] Explaining Temporal Graph Models through an Explorer-Navigator Framework [paper]
16. [ICLR 23] DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks [paper]
17. [ICLR 23] Interpretable Geometric Deep Learning via Learnable Randomness Injection [paper]
18. [ICLR 23] A Differential Geometric View and Explainability of GNN on Evolving Graphs [paper]
19. [KDD 23] MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation [paper]
20. [KDD 23] Counterfactual Learning on Heterogeneous Graphs with Greedy Perturbation [paper]
21. [KDD 23] Empower Post-hoc Graph Explanations with Information Bottleneck: A Pre-training and Fine-tuning Perspective[paper]
22. [KDD 23] Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining.[paper]
23. [KDD 23] Shift-Robust Molecular Relational Learning with Causal Substructure [paper]
24. [AAAI 23] Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis [paper]
25. [AAAI 23] On the Limit of Explaining Black-box Temporal Graph Neural Networks [paper]
26. [AAAI 23] Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network [paper]
27. [AAAI 23] Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery [paper]
28. [VLDB 23] HENCE-X: Toward Heterogeneity-agnostic Multi-level Explainability for Deep Graph Networks [paper]
29. [VLDB 23] On Data-Aware Global Explainability of Graph Neural Networks [paper]
30. [AISTATS 23] Distill n' Explain: explaining graph neural networks using simple surrogates [Paper]
31. [AISTATS 23] Probing Graph Representations [paper]
32. [ICDE 23] INGREX: An Interactive Explanation Framework for Graph Neural Networks[paper]
33. [ICDE 23] Jointly Attacking Graph Neural Network and its Explanations [paper]
34. [WWW 23]PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction [paper]
35. [ICDM 23] Limitations of Perturbation-based Explanation Methods for Temporal Graph Neural Networks
36. [ICDM 23] Interpretable Subgraph Feature Extraction for Hyperlink Prediction[paper]
37. [WSDM 23]Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs [paper]
38. [WSDM 23]Cooperative Explanations of Graph Neural Networks [paper]
39. [WSDM 23]Towards Faithful and Consistent Explanations for Graph Neural Networks [paper]
40. [WSDM 23] Global Counterfactual Explainer for Graph Neural Networks [paper]
41. [CIKM 23] Explainable Spatio-Temporal Graph Neural Networks [paper]
42. [CIKM 23] DuoGAT: Dual Time-oriented Graph Attention Networks for Accurate, Efficient and Explainable Anomaly Detection on Time-series. [paper]
43. [CIKM 23] Heterogeneous Temporal Graph Neural Network Explainer [paper]
44. [CIKM 23] ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks[paper]
45. [CIKM 23] KG4Ex: An Explainable Knowledge Graph-Based Approach for Exercise Recommendation [paper]
46. [ECML-PKDD 23] ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning [paper]
47. [TPAMI 23] FlowX: Towards Explainable Graph Neural Networks via Message Flows [paper]
48. [TAI] Prototype-based interpretable graph neural networks. [paper]
49. [TKDE 23] Counterfactual Graph Learning for Anomaly Detection on Attributed Networks [paper]
50. [Scientific Data 23 ] Evaluating explainability for graph neural networks [paper]
51. [Nature Communications 23] Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking [paper]
52. [ACM Computing Surveys 23] A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation [paper]
53. [TIST 23] Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment [paper]
54. [GLFrontiers 23] Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts [paper]
55. [Openreview 23] Iterative Graph Neural Network Enhancement Using Explanations [paper]
56. [NeurIPS 2023 Workshop XAIA] GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Networks Explanations [paper]
57. [NeurIPS 2023 Workshop XAIA] On the Consistency of GNN Explainability Methods [paper]
58. [AICS 23] A subgraph interpretation generative model for knowledge graph link prediction based on uni-relation transformation [paper]
59. [GUT 23] Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study [paper]
60. [PR 2023] Towards self-explainable graph convolutional neural network with frequency adaptive inception [paper]
61. [MLG 2023] Understanding how explainers work in graph neural networks [paper]
62. [MLG 2023] Graph Model Explainer Tool [paper]
63. [Information Science 23] Robust explanations for graph neural network with neuron explanation component [paper]
64. [Recsys 23] Explainable Graph Neural Network Recommenders; Challenges and Opportunities [paper]
65. [xAI 23] Counterfactual Explanations for Graph Classification Through the Lenses of Density [paper]
66. [XAI 23] Evaluating Link Prediction Explanations for Graph Neural Networks [[paper]](https://arxiv.org/abs/2308.01682
67. [xAI 23] XInsight: Revealing Model Insights for GNNs with Flow-based Explanations [paper]
68. [xAI 23] Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies [paper]
69. [xAI 23] MEGAN: Multi Explanation Graph Attention Network [paper]
70. [XKDD 23] Game Theoretic Explanations for Graph Neural Networks [paper]
71. [XKDD 23] From Black Box to Glass Box: Evaluating Faithfulness of Process Predictions with GCNNs [paper]
72. [IJCNN 23] MEGA: Explaining Graph Neural Networks with Network Motifs [paper]
73. [LOG Poster 23] On the Robustness of Post-hoc GNN Explainers to Label Noise [paper]
74. [LOG Poster 23] How Faithful are Self-Explainable GNNs? [paper]
75. [LOG Poster 23] Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples [paper]
76. [ICAID 2023] Explanations for Graph Neural Networks via Layer Analysis. [paper]
77. [ECAI 23] XGBD: Explanation-Guided Graph Backdoor Detection [paper]
78. [IEEE Transactions on Consumer Electronics 23] Human Pose Prediction Using Interpretable Graph Convolutional Network for Smart Home [paper]
79. [KBS 23] KE-X: Towards subgraph explanations of knowledge graph embedding based on knowledge information gain [paper]
80. [ICML workshop 23] Generating Global Factual and Counterfactual Explainer for Molecule under Domain Constraints [paper]
81. [Thesis 23] Developing interpretable graph neural networks for high dimensional feature spaces [paper]
82. [Thesis 23] Evaluation of Explainability Methods on Single-Cell Classification Tasks Using Graph Neural Networks [paper]
83. [ISSTA23] Interpreters for GNN-Based Vulnerability Detection: Are We There Yet? [paper]
84. [ICECAI23] Improved GraphSVX for GNN Explanations Based on Cross Entropy [paper]
85. [ICRA Workshop 23] Towards Semantic Interpretation and Validation of Graph Attention-based Explanations [paper]
86. [Thesis 23] Interpretability of Graphical Models [paper]
87. [Bioengineering 2023] Personalized Explanations for Early Diagnosis of Alzheimer's Disease Using Explainable Graph Neural Networks with Population Graphs [paper]
88. [BDSC 2023] MDC: An Interpretable GNNs Method Based on Node Motif Degree and Graph Diffusion Convolution [[paper]] (https://link.springer.com/chapter/10.1007/978-981-99-3925-1_24)
89. [Information Science 2023] Explainability techniques applied to road traffic forecasting using Graph Neural Network models [paper]
90. [Arxiv 23.05] EiX-GNN : Concept-level eigencentrality explainer for graph neural networks [paper]
91. [ICLR Tiny 23] Message-passing selection: Towards interpretable GNNs for graph classification [paper]
92. [ICLR Tiny 23] Revisiting CounteRGAN for Counterfactual Explainability of Graphs [paper]
93. [MICCAI Workshop 23] IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction [paper]
94. [GRADES & NDA'23] A Demonstration of Interpretability Methods for Graph Neural Networks [paper]
95. [Arxiv 23] Self-Explainable Graph Neural Networks for Link Prediction [paper]
96. [Arxiv 23.02] MotifExplainer: a Motif-based Graph Neural Network Explainer [paper]
97. [ChemRxiv 23] Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches [paper]
98. [Neural Networks 23] Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness [paper]
99. [ICASSP 23] Towards a More Stable and General Subgraph Information Bottleneck [paper]
100. [ESANN 23] Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability [Paper]
101. [IEEE Access] Generating Real-Time Explanations for GNNs via Multiple Specialty Learners and Online Knowledge Distillation [Paper]
102. [IEEE Access] Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions [paper]
103. [Journal of Software 23] A Slice-level vulnerability detection and interpretation method based on graph neural network [paper]
104. [Automation in Construction 23] Learning from explainable data-driven tunneling graphs: A spatio-temporal graph convolutional network for clogging detection [paper]
105. [Briefings in Bioinformatics] Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism [paper]
106. [Briefings in Bioinformatics] Identification of vital chemical information via visualization of graph neural networks [paper]
107. [Bioinformatics 23] Explainable Multilayer Graph Neural Network for Cancer Gene Prediction [paper]
108. [ICLR Workshop 23] GCI: A Graph Concept Interpretation Framework [paper]
109. [Arxiv 23] Structural Explanations for Graph Neural Networks using HSIC [paper]
110. [Internet of Things 23] XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics [paper]
111. [JOS23] A Generic Explaining & Locating Method for Malware Detection based on Graph Neural Networks [paper]
112. [IJCNN 23] GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs [paper]
113. [NLDL 23] Explainability in subgraphs-enhanced Graph Neural Networks [paper]
114. [ICAAAIML 23] SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods [paper]

Year 2022

1. [NeurIPS 22] GStarX:Explaining Graph-level Predictions with Communication Structure-Aware Cooperative Games [paper]
2. [NeurIPS 22] Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure [paper]
3. [NeurIPS 22] Task-Agnostic Graph Neural Explanations [paper]
4. [NeurIPS 22] CLEAR: Generative Counterfactual Explanations on Graphs[paper]
5. [ICML 22] Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism [paper]
6. [ICLR 22] DEGREE: Decomposition Based Explanation for Graph Neural Networks [paper]
7. [ICLR 22] Explainable GNN-Based Models over Knowledge Graphs [paper]
8. [ICLR 22] Discovering Invariant Rationales for Graph Neural Networks [paper]
9. [KDD 22] On Structural Explanation of Bias in Graph Neural Networks [paper]
10. [KDD 22] Causal Attention for Interpretable and Generalizable Graph Classification [paper]
11. [CVPR 22] OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks [paper]
12. [CVPR 22] Improving Subgraph Recognition with Variational Graph Information Bottleneck [paper]
13. [AISTATS 22] Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods [paper]
14. [AISTATS 22] CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks [paper]
15. [TPAMI 22] Differentially Private Graph Neural Networks for Whole-Graph Classification [paper]
16. [TPAMI 22] Reinforced Causal Explainer for Graph Neural Networks [paper]
17. [VLDB 22] xFraud: Explainable Fraud Transaction Detection on Heterogeneous Graphs [paper]
18. [LOG 22]GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks [paper]
19. [LOG 22] Towards Training GNNs using Explanation Directed Message Passing [paper]
20. [The Webconf 22] Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning [paper]
21. [AAAI 22] Prototype-Based Explanations for Graph Neural Networks [paper]
22. [AAAI 22] KerGNNs: Interpretable Graph Neural Networks with Graph Kernels[paper]
23. [AAAI 22] ProtGNN: Towards Self-Explaining Graph Neural Networks [paper]
24. [IEEE Big Data 22] Trade less Accuracy for Fairness and Trade-off Explanation for GNN [paper]
25. [CIKM 22] GRETEL: Graph Counterfactual Explanation Evaluation Framework[paper]
26. [CIKM 22] A Model-Centric Explainer for Graph Neural Network based Node Classification [paper]
27. [IJCAI 22] What Does My GNN Really Capture? On Exploring Internal GNN Representations [paper]
28. [ECML PKDD 22] Improving the quality of rule-based GNN explanations [paper]
29. [MICCAI 22] Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis [paper]
30. [MICCAI 22] Sparse Interpretation of Graph Convolutional Networks for Multi-modal Diagnosis of Alzheimer’s Disease [paper]
31. [EuroS&P 22] Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis [paper]
32. [INFOCOM 22] Interpretability Evaluation of Botnet Detection Model based on Graph Neural Network [paper]
33. [GLOBECOM 22] Shapley Explainer - An Interpretation Method for GNNs Used in SDN [paper]
34. [GLOBECOM 22] An Explainer for Temporal Graph Neural Networks [paper]
35. [TKDE 22] Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks [paper]
36. [TNNLS 22] Interpretable Graph Reservoir Computing With the Temporal Pattern Attention [paper]
37. [TNNLS22] A Meta-Learning Approach for Training Explainable Graph Neural Networks [paper]
38. [TNNLS 22] Explaining Deep Graph Networks via Input Perturbation [paper]
39. [DMKD 22] On GNN explanability with activation patterns [paper]
40. [KBS 22] EGNN: Constructing explainable graph neural networks via knowledge distillation [paper]
41. [XKDD 22] GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations [paper]
42. [AI 22] Are Graph Neural Network Explainers Robust to Graph Noises? [paper]
43. [BRACIS 22] ConveXplainer for Graph Neural Networks [paper]
44. [GLB 22] An Explainable AI Library for Benchmarking Graph Explainers [paper]
45. [DASFAA 22] On Global Explainability of Graph Neural Networks [paper]
46. [ISBI 22] Interpretable Graph Convolutional Network Of Multi-Modality Brain Imaging For Alzheimer’s Disease Diagnosis [paper]
47. [Bioinformatics] GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks [paper]
48. [Medical Imaging 2022] Phenotype guided interpretable graph convolutional network analysis of fMRI data reveals changing brain connectivity during adolescence [paper]
49. [NeuroComputing 22] Perturb more, trap more: Understanding behaviors of graph neural networks [paper]
50. [DSN 22] CFGExplainer: Explaining Graph Neural Network-Based Malware Classification from Control Flow Graphs [paper]
51. [IEEE Access 22] Providing Node-level Local Explanation for node2vec through Reinforcement Learning [paper]
52. [Patterns 22] Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction [paper]
53. [IEEE Access 22] Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions [paper]
54. [IEEE 22] Explaining Graph Neural Networks With Topology-Aware Node Selection: Application in Air Quality Inference [paper]
55. [IEEE Robotics and Automation Letters 22] Efficient and Interpretable Robot Manipulation with Graph Neural Networks [paper]
56. [Arxiv 22] Deconfounding to Explanation Evaluation in Graph Neural Networks [paper]
57. [ICCPR 22] GANExplainer: GAN-based Graph Neural Networks Explainer [paper]
58. [Arxiv 22] Exploring Explainability Methods for Graph Neural Networks [paper]
59. [Openreview 22] TGP: Explainable Temporal Graph Neural Networks for Personalized Recommendation [paper]
60. [Arxiv 22] PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes [paper]
61. [Arxiv 22] Defending Against Backdoor Attack on Graph Neural Network by Explainability [paper]
62. [Arxiv 22] Faithful Explanations for Deep Graph Models [paper]
63. [Arxiv 22] Towards Explanation for Unsupervised Graph-Level Representation Learning [paper]
64. [Arxiv 22] Explainability in Graph Neural Networks: An Experimental Survey [paper]
65. [IEEE TSIPN 22] Explainability and Graph Learning from Social Interactions [paper]

Year 2021

1. [NeurIPS 21] SALKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning [paper]
2. [NeurIPS 2021] Reinforcement Learning Enhanced Explainer for Graph Neural Networks [paper]
3. [NeurIPS 2021] Towards Multi-Grained Explainability for Graph Neural Networks [paper]
4. [NeurIPS 2021] Robust Counterfactual Explanations on Graph Neural Networks [paper]
5. [ICML 2021] On Explainability of Graph Neural Networks via Subgraph Explorations[paper]
6. [ICML 2021] Generative Causal Explanations for Graph Neural Networks[paper]
7. [ICML 2021] Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity[paper]
8. [ICML 2021] Automated Graph Representation Learning with Hyperparameter Importance Explanation[paper]
9. [ICLR 21] Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs [paper]
10. [ICLR 2021] Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking[paper]
11. [ICLR 2021] Graph Information Bottleneck for Subgraph Recognition [paper]
12. [KDD 2021] When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods[paper]
13. [KDD 2021] Counterfactual Graphs for Explainable Classification of Brain Networks [paper]
14. [CVPR 2021] Quantifying Explainers of Graph Neural Networks in Computational Pathology.[paper]
15. [NAACL 2021] Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network. [paper]
16. [AAAI 2021] Motif-Driven Contrastive Learning of Graph Representations [paper]
17. [TPAMI 21] Higher-Order Explanations of Graph Neural Networks via Relevant Walks [paper]
18. [WWW 2021] Interpreting and Unifying Graph Neural Networks with An Optimization Framework [paper]
19. [Genome medicine 21] Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer [paper]
20. [IJCKG 21] Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules [paper]
21. [RuleML+RR 21] Combining Sub-Symbolic and Symbolic Methods for Explainability [paper]
22. [PAKDD 21] SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction [paper]
23. [J. Chem. Inf. Model] Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment [paper]
24. [BioRxiv 21] APRILE: Exploring the Molecular Mechanisms of Drug Side Effects with Explainable Graph Neural Networks [paper]
25. [ISM 21] Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks [paper]
26. [Arxiv 21] Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows [paper]
27. [Arxiv 21] Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation [paper]
28. [Arxiv 21] Learnt Sparsification for Interpretable Graph Neural Networks [paper]
29. [ICML workshop 21] GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks [paper]
30. [ICML workshop 21] Reliable Graph Neural Network Explanations Through Adversarial Training [paper]
31. [ICML workshop 21] Reimagining GNN Explanations with ideas from Tabular Data [paper]
32. [ICML workshop 21] Towards Automated Evaluation of Explanations in Graph Neural Networks [paper]
33. [ICDM 2021] GNES: Learning to Explain Graph Neural Networks [paper]
34. [ICDM 2021] GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs [paper]
35. [ICDM 2021] Multi-objective Explanations of GNN Predictions [paper]
36. [CIKM 2021] Towards Self-Explainable Graph Neural Network [paper]
37. [ECML PKDD 2021] GraphSVX: Shapley Value Explanations for Graph Neural Networks [paper]
38. [WiseML 2021] Explainability-based Backdoor Attacks Against Graph Neural Networks [paper]
39. [IJCNN 21] MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks [paper]
40. [ICCSA 2021] Understanding Drug Abuse Social Network Using Weighted Graph Neural Networks Explainer [paper]
41. [NeSy 21] A New Concept for Explaining Graph Neural Networks [paper]
42. [Information Fusion 21] Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI [paper]
43. [Patterns 21] hcga: Highly Comparative Graph Analysis for network phenotyping [paper]
44. [Neural Networks 21] Understanding the Message Passing in Graph Neural Networks via Power Iteration [paper]

Year 2020 and Before

1. [NeurIPS 2020] Parameterized Explainer for Graph Neural Network.[paper]
2. [NeurIPS 2020] PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks [paper]
3. [KDD 2020] XGNN: Towards Model-Level Explanations of Graph Neural Networks [paper]
4. [ACL 2020]GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. paper
5. [Arxiv 2020] Graph Neural Networks Including Sparse Interpretability [paper]
6. [NeurIPS Workshop 20] Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks [paper]
7. [ICML workstop 2020] Contrastive Graph Neural Network Explanation [paper]
8. [ICML workstop 2020] Towards Explainable Graph Representations in Digital Pathology [paper]
9. [NeurIPS workshop 2020] Explaining Deep Graph Networks with Molecular Counterfactuals [paper]
10. [DataMod 2020] Exploring Graph-Based Neural Networks for Automatic Brain Tumor Segmentation" [paper]
11. [OpenReview 20] A Framework For Differentiable Discovery Of Graph Algorithms [paper]
12. [OpenReview 20] Causal Screening to Interpret Graph Neural Networks [paper]
13. [Arxiv 20] Understanding Graph Neural Networks from Graph Signal Denoising Perspectives [paper]
14. [IJCNN 20] GCN-LRP explanation: exploring latent attention of graph convolutional networks] [paper]
15. [CD-MAKE 20] Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification [paper]
16. [ICDM 19] Scalable Explanation of Inferences on Large Graphs[paper]
17. [NeurIPS 19]GNNExplainer: Generating Explanations for Graph Neural Networks[paper]