TSNN equips temporal graphs with per-node time-varying orthogonal frames, explicit transport, and a geometric-residual decoder, delivering competitive or superior link prediction on benchmarks plus theoretical guarantees on sheaf diffusion.
InProceedings of the 32nd ACM International Con- ference on Information and Knowledge Management(Birmingham, United King- dom)(CIKM ’23)
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HADES adapts knowledge distillation for hypergraph neural networks by using quantified node heterophily as a proxy for teacher reliability, yielding student models that often outperform teachers with up to 12.3x faster inference.
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Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport
TSNN equips temporal graphs with per-node time-varying orthogonal frames, explicit transport, and a geometric-residual decoder, delivering competitive or superior link prediction on benchmarks plus theoretical guarantees on sheaf diffusion.
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Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks
HADES adapts knowledge distillation for hypergraph neural networks by using quantified node heterophily as a proxy for teacher reliability, yielding student models that often outperform teachers with up to 12.3x faster inference.