Oversmoothing in neural sheaf diffusion is reframed as representation degeneration in the incidence-quiver harmonic space, with moment-map regularizers and non-uniform stalk dimensions proposed to avoid it.
International Conference on Learning Representations , year =
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Under strict inductive protocols without temporal leakage, random forests on raw features achieve higher F1 scores than GNNs on Bitcoin fraud detection, and real graph structure can underperform random wiring.
Learned policies for short-term-to-long-term memory transfer in temporal knowledge graphs outperform baselines on the RoomKG benchmark with capacity 128.
UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.
citing papers explorer
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Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion
Oversmoothing in neural sheaf diffusion is reframed as representation degeneration in the incidence-quiver harmonic space, with moment-map regularizers and non-uniform stalk dimensions proposed to avoid it.
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When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift
Under strict inductive protocols without temporal leakage, random forests on raw features achieve higher F1 scores than GNNs on Bitcoin fraud detection, and real graph structure can underperform random wiring.
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Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
Learned policies for short-term-to-long-term memory transfer in temporal knowledge graphs outperform baselines on the RoomKG benchmark with capacity 128.
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UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.