Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective | Paper Citation Record

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This paper cites Evaluating explainability for graph neural networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Evaluating explainability for graph neural networks

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This paper cites H., and Lakkaraju, H.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective H., and Lakkaraju, H

Reference 2

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This paper cites Graphframex: Towards systematic evaluation of explainability methods for graph neural networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Graphframex: Towards systematic evaluation of explainability methods for graph neural networks

Reference 3

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This paper cites Global explainability of gnns via logic combination of learned concepts.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Global explainability of gnns via logic combination of learned concepts

Reference 4

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This paper cites Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs

Reference 5

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This paper cites and Albert, R.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective and Albert, R

Reference 6

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This paper cites Entropy-based logic explanations of neural networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Entropy-based logic explanations of neural networks

Reference 7

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This paper cites V., Monet, M., P \'e rez, J., Reutter, J., and Silva, J.-P.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective V., Monet, M., P \'e rez, J., Reutter, J., and Silva, J.-P

Reference 8

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This paper cites Graph Neural Networks Use Graphs When They Shouldn't.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Graph Neural Networks Use Graphs When They Shouldn't

Reference 9

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This paper cites The intelligible and effective graph neural additive network.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective The intelligible and effective graph neural additive network

Reference 10

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This paper cites Causal Explanations and XAI.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Causal Explanations and XAI

Reference 11

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This paper cites Auditing Local Explanations is Hard.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Auditing Local Explanations is Hard

Reference 12

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This paper cites Learning causally invariant representations for out-of-distribution generalization on graphs.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Learning causally invariant representations for out-of-distribution generalization on graphs

Reference 13

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This paper cites How interpretable are interpretable graph neural networks? In Forty-first International Conference on Machine Learning, 2024.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective How interpretable are interpretable graph neural networks? In Forty-first International Conference on Machine Learning, 2024

Reference 14

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This paper cites How Faithful are Self-Explainable GNNs?.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective How Faithful are Self-Explainable GNNs?

Reference 15

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This paper cites and Wang, S.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective and Wang, S

Reference 16

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This paper cites Towards Prototype-Based Self-Explainable Graph Neural Network.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Towards Prototype-Based Self-Explainable Graph Neural Network

Reference 17

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This paper cites and Hirth, A.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective and Hirth, A

Reference 18

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This paper cites K., Lopez de Compadre, R.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective K., Lopez de Compadre, R

Reference 19

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This paper cites and Shen, Y.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective and Shen, Y

Reference 20

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This paper cites On random graphs i.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective On random graphs i

Reference 21

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This paper cites A Self-Explainable Heterogeneous GNN for Relational Deep Learning.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective A Self-Explainable Heterogeneous GNN for Relational Deep Learning

Reference 22

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This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Fast Graph Representation Learning with PyTorch Geometric

Reference 23

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Xai and bias of deep graph networks

Reference 24

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This paper cites C., Li \'o , P., and Barbiero, P.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective C., Li \'o , P., and Barbiero, P

Reference 25

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective V., Gonzalez, G., and Agarwal, C

Reference 26

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This paper cites Anti-symmetric dgn: a stable architecture for deep graph networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Anti-symmetric dgn: a stable architecture for deep graph networks

Reference 27

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This paper cites The logic of graph neural networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective The logic of graph neural networks

Reference 28

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This paper cites GOOD : A graph out-of-distribution benchmark.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective GOOD : A graph out-of-distribution benchmark

Reference 29

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This paper cites Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Reference 30

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Addressing leakage in concept bottleneck models

Reference 31

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Sat-based formula simplification

Reference 32

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This paper cites On relating explanations and adversarial examples.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective On relating explanations and adversarial examples

Reference 33

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This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 34

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This paper cites Categorical Reparameterization with Gumbel-Softmax.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Categorical Reparameterization with Gumbel-Softmax

Reference 35

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This paper cites Multi-objective molecule generation using interpretable substructures.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Multi-objective molecule generation using interpretable substructures

Reference 36

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Unresolved cited work

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective P., Mesiar, R., and Pap, E

Reference 38

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective W., and Amer, M

Reference 39

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This paper cites Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks

Reference 40

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This paper cites Estimating mutual information.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Estimating mutual information

Reference 41

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Explainable Graph Neural Networks Under Fire

Reference 42

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Graph Neural Networks Including Sparse Interpretability

Reference 43

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Explaining the explainers in graph neural networks: a comparative study

Reference 44

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Parameterized explainer for graph neural network

Reference 45

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Do Concept Bottleneck Models Learn as Intended?

Reference 46

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Logic-based explainability in machine learning

Reference 47

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective and Ignatiev, A

Reference 48

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective C., Ignatiev, A., and Narodytska, N

Reference 49

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective F., Teixeira, A

Reference 50

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This paper cites and Stratos, K.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective and Stratos, K

Reference 51

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This paper cites Interpretable and generalizable graph learning via stochastic attention mechanism.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Interpretable and generalizable graph learning via stochastic attention mechanism

Reference 52

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This paper cites Interpretable geometric deep learning via learnable randomness injection.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Interpretable geometric deep learning via learnable randomness injection

Reference 53

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This paper cites TUDataset: A collection of benchmark datasets for learning with graphs.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective TUDataset: A collection of benchmark datasets for learning with graphs

Reference 54

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This paper cites Graphchef: Learning the recipe of your dataset.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Graphchef: Learning the recipe of your dataset

Reference 55

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This paper cites Automatic differentiation in pytorch.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Automatic differentiation in pytorch

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.658472Z

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This paper cites Logical distillation of graph neural networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Logical distillation of graph neural networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.626694Z

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This paper cites Prototype-based interpretable graph neural networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Prototype-based interpretable graph neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.617898Z

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This paper cites and Bunke, H.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective and Bunke, H

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.609073Z

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This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.600180Z

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This paper cites C., Hagenbuchner, M., and Monfardini, G.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective C., Hagenbuchner, M., and Monfardini, G

Reference 61

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This paper cites L2XGNN: Learning to Explain Graph Neural Networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective L2XGNN: Learning to Explain Graph Neural Networks

Reference 62

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This paper cites Glocalx-from local to global explanations of black box ai models.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Glocalx-from local to global explanations of black box ai models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.585550Z

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This paper cites A Symbolic Approach to Explaining Bayesian Network Classifiers.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective A Symbolic Approach to Explaining Bayesian Network Classifiers

Reference 64

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Axiomatic attribution for deep networks

Reference 65

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This paper cites A., Poda, G., and Tetko, I.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective A., Poda, G., and Tetko, I

Reference 66

Resolution: verified exact; doi, observed 2026-08-09T11:32:26.942656Z

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This paper cites Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning

Reference 67

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Observation ee8e16b9-b2dd-402b-9e62-36d4ca2d2f21 · outbound

This paper cites Leveraging explanations in interactive machine learning: An overview.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Leveraging explanations in interactive machine learning: An overview

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.570677Z

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Observation 6893d6b9-b50b-42ad-b7a5-3198e2fd45ea · outbound

This paper cites The information bottleneck method.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective The information bottleneck method

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-09T11:32:26.871309Z

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This paper cites Probabilistic sufficient explanations.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Probabilistic sufficient explanations

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.561459Z

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Observation c8362ede-e8c0-47fc-a8b6-13bbf51c9bf0 · outbound

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:32:27.552672Z

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Observation 57f6ee78-059e-4adf-94dd-f54bbc5958d0 · outbound

This paper cites J., Valeri, J.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective J., Valeri, J

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.544310Z

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This paper cites Graph information bottleneck.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Graph information bottleneck

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.536250Z

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This paper cites Discovering Invariant Rationales for Graph Neural Networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Discovering Invariant Rationales for Graph Neural Networks

Reference 74

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This paper cites N., Gomes, J., Geniesse, C., Pappu, A.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective N., Gomes, J., Geniesse, C., Pappu, A

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T11:32:26.888893Z

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Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:32:27.522979Z

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This paper cites How Powerful are Graph Neural Networks?.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective How Powerful are Graph Neural Networks?

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-09T11:32:26.894601Z

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Observation 3ed7630c-1dff-436e-afd6-b0f95e2aa488 · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Gnnexplainer: Generating explanations for graph neural networks

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-09T11:32:26.897701Z

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This paper cites Graph information bottleneck for subgraph recognition.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Graph information bottleneck for subgraph recognition

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.489152Z

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Observation b6b8a489-cb3d-4db0-8dbc-c675a7a7cd07 · outbound

This paper cites Improving subgraph recognition with variational graph information bottleneck.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Improving subgraph recognition with variational graph information bottleneck

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.434516Z

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Observation 8abc4f26-9b18-421b-bb07-2310f9a69fbf · outbound

This paper cites On Formal Feature Attribution and Its Approximation.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective On Formal Feature Attribution and Its Approximation

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-09T11:32:26.906117Z

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Observation 5e58e007-3b5c-4d09-b058-91ae22b8775e · outbound

This paper cites On explainability of graph neural networks via subgraph explorations.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective On explainability of graph neural networks via subgraph explorations

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.380730Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e074d0ef-ffe2-42d0-bbae-4e61cf3b2031 · outbound

This paper cites Explainability in graph neural networks: A taxonomic survey.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Explainability in graph neural networks: A taxonomic survey

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-09T11:32:26.912297Z

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Observation fbec7689-21b7-43d5-8d49-29a1b6d7506e · outbound

This paper cites Protgnn: Towards self-explaining graph neural networks.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Protgnn: Towards self-explaining graph neural networks

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:32:27.320774Z

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