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.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
citing papers explorer
-
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.