FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
Interpreting graph neural networks for nlp with differentiable edge masking
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Clue2Group recovers laundering groups from initial clues via local context, GNN-based risk fields, and evidence integration on AML benchmarks.
B-cos GNNs replace non-linear message and update functions with B-cos transforms in GNNs to enable exact per-node per-feature explanations from a single forward-backward pass while retaining competitive accuracy.
TACENR introduces a contrastive-learning method that identifies the most influential attribute, proximity, and structural features in node representations in a task-agnostic manner.
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
citing papers explorer
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FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
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Clue-Guided Money Laundering Group Discovery
Clue2Group recovers laundering groups from initial clues via local context, GNN-based risk fields, and evidence integration on AML benchmarks.
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B-cos GNNs: Faithful Explanations through Dynamic Linearity
B-cos GNNs replace non-linear message and update functions with B-cos transforms in GNNs to enable exact per-node per-feature explanations from a single forward-backward pass while retaining competitive accuracy.
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TACENR: Task-Agnostic Contrastive Explanations for Node Representations
TACENR introduces a contrastive-learning method that identifies the most influential attribute, proximity, and structural features in node representations in a task-agnostic manner.
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Explaining the Explainers in Graph Neural Networks: a Comparative Study
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.