ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
Pattern Recognition , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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FLO-EMD integrates flow-guided attention and EMD on aggregated motion traces to classify light, medium, and heavy congestion at 97.5% accuracy on 1,050 surveillance clips.
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ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
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Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decomposition
FLO-EMD integrates flow-guided attention and EMD on aggregated motion traces to classify light, medium, and heavy congestion at 97.5% accuracy on 1,050 surveillance clips.