Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls | Paper Citation Record

Observation 3b66b22d-aeef-4406-a146-bc6d31755e69 · outbound

This paper cites Active and semi-supervised graph neural networks for graph classification,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Active and semi-supervised graph neural networks for graph classification,

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This paper cites Semisuper- vised graph neural networks for graph classification,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Semisuper- vised graph neural networks for graph classification,

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This paper cites Edge classification on graphs: New directions in topological imbalance,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Edge classification on graphs: New directions in topological imbalance,

Reference 3

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This paper cites A simple yet effective baseline for non-attributed graph classification.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls A simple yet effective baseline for non-attributed graph classification

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

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls TUDataset: A collection of benchmark datasets for learning with graphs

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This paper cites Protein function prediction via graph kernels,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Protein function prediction via graph kernels,

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This paper cites Spline-fitting with a genetic algorithm: A method for developing classification structure activity relationships,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Spline-fitting with a genetic algorithm: A method for developing classification structure activity relationships,

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This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Towards A Rigorous Science of Interpretable Machine Learning

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

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Explainability in graph neural networks: A taxonomic survey,

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Observation a4cd5cfe-2c16-4bd3-a63c-9e657fc4bd1b · outbound

This paper cites Gnnex- plainer: Generating explanations for graph neural networks,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Gnnex- plainer: Generating explanations for graph neural networks,

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This paper cites Xgnn: Towards model-level explanations of graph neural networks,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Xgnn: Towards model-level explanations of graph neural networks,

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Observation 56d3e7e9-a688-439a-99c8-3b1f85e66127 · outbound

This paper cites GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks

Reference 12

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This paper cites D4explainer: In-distribution explanations of graph neural network via discrete denoising diffusion,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls D4explainer: In-distribution explanations of graph neural network via discrete denoising diffusion,

Reference 13

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This paper cites Gnnboundary: Towards explaining graph neural networks through the lens of decision boundaries,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Gnnboundary: Towards explaining graph neural networks through the lens of decision boundaries,

Reference 14

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Observation 87b2db0d-6851-4c6d-814e-f035085b9bec · outbound

This paper cites Graphon-explainer: Generating model- level explanations for graph neural networks using graphons,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Graphon-explainer: Generating model- level explanations for graph neural networks using graphons,

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This paper cites Graphlime: Local interpretable model explanations for graph neural networks,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Graphlime: Local interpretable model explanations for graph neural networks,

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This paper cites Eig-search: Generating edge-induced subgraphs for gnn explanation in linear time,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Eig-search: Generating edge-induced subgraphs for gnn explanation in linear time,

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This paper cites Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking

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Observation b280d936-f48f-4c5b-8e32-18fa546b6853 · outbound

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

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls On explainability of graph neural networks via subgraph explorations,

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This paper cites Parameterized explainer for graph neural network,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Parameterized explainer for graph neural network,

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This paper cites Zhang and G.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Zhang and G

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This paper cites Network flows: Theory, algorithms, and applications,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Network flows: Theory, algorithms, and applications,

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This paper cites A fast adaptive k-means with no bounds,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls A fast adaptive k-means with no bounds,

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This paper cites An efficient and adaptive granular-ball generation method in classification problem,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls An efficient and adaptive granular-ball generation method in classification problem,

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This paper cites Online graph dictionary learning.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Online graph dictionary learning

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Semi-Supervised Classification with Graph Convolutional Networks

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Observation f895539e-2ef9-4ce6-8d48-3f2dc4a72408 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls How Powerful are Graph Neural Networks?

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This paper cites Explainability methods for graph convolutional neural networks,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Explainability methods for graph convolutional neural networks,

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This paper cites Explainability Techniques for Graph Convolutional Networks.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Explainability Techniques for Graph Convolutional Networks

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This paper cites DEGREE: Decomposition Based Explanation For Graph Neural Networks.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls DEGREE: Decomposition Based Explanation For Graph Neural Networks

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This paper cites Graphsvx: Shapley value explanations for graph neural networks,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Graphsvx: Shapley value explanations for graph neural networks,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Dis- till n’explain: explaining graph neural networks using simple surrogates,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Relex: A model-agnostic relational model explainer,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Towards multi- grained explainability for graph neural networks,

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This paper cites Zorro: Valid, sparse, and stable explanations in graph neural networks,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Zorro: Valid, sparse, and stable explanations in graph neural networks,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Gstarx: Explaining graph neural networks with structure-aware cooperative games,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Topological structure in visual perception,

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This paper cites Dgcc: data-driven granular cognitive computing,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Dgcc: data-driven granular cognitive computing,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Gb-rvfl: Fusion of randomized neural network and granular ball computing,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Ball kk-means: Fast adaptive clustering with no bounds,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Gbct: efficient and adaptive clustering via granular-ball computing for complex data,

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This paper cites Generation of granular-balls for clus- tering based on the principle of justifiable granularity,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Generation of granular-balls for clus- tering based on the principle of justifiable granularity,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Granular-ball regeneration clustering with principle of justifiable gran- ularity,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Aw-gbgae: an adaptive weighted graph autoencoder based on granular-balls for general data clustering,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Gbrs: A unified granular-ball learning model of pawlak rough set and neighborhood rough set,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Constructing three-way decision with fuzzy granular-ball rough sets based on uncertainty invariance,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Fuzzy granule density-based outlier detection with multi-scale granular balls,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Graph coarsening via supervised granular-ball for scalable graph neural network training,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Unresolved cited work

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls A new internal index based on density core for clustering validation,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls The upper bound of the optimal number of clusters in fuzzy clustering,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Unresolved cited work

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Structure-activity relationship of muta- genic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Derivation and validation of tox- icophores for mutagenicity prediction,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Comparison of descriptor spaces for chemical compound retrieval and classification,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Fast unfolding of communities in large networks,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings,

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Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Towards better understanding of gradient-based attribution methods for Deep Neural Networks

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This paper cites Explainability methods for graph convolutional neural networks,.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls Explainability methods for graph convolutional neural networks,

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