Proposes the I2B-HGNN framework using information bottleneck-guided graph transformers and heterogeneous graph attention for interpretable multimodal NDD diagnosis.
Hierarchical graph representation learning with differentiable pooling
2 Pith papers cite this work. Polarity classification is still indexing.
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Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
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Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis
Proposes the I2B-HGNN framework using information bottleneck-guided graph transformers and heterogeneous graph attention for interpretable multimodal NDD diagnosis.
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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.