Spectra defines and controls effective capacity in graph embeddings via the Shannon effective rank of a trace-normalized kernel spectrum, making capacity a post-fit property rather than a pre-training hyperparameter.
What do gnns actually learn? towards understanding their representations
3 Pith papers cite this work. Polarity classification is still indexing.
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Graph nodes are embedded as simplex compositions via ILR coordinates in Aitchison geometry to obtain interpretable representations that support component restriction and competitive task performance.
TACENR introduces a contrastive-learning method that identifies the most influential attribute, proximity, and structural features in node representations in a task-agnostic manner.
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Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models
Spectra defines and controls effective capacity in graph embeddings via the Shannon effective rank of a trace-normalized kernel spectrum, making capacity a post-fit property rather than a pre-training hyperparameter.