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researcher

Piero Deidda

4 papers hereh-index 340 citations14 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • math.DS1
  • math.SP1

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2025

Stuart-Landau Oscillatory Graph Neural Network

Kaicheng Zhang, David N. Reynolds, Piero Deidda +1

Oscillatory Graph Neural Networks (OGNNs) are an emerging class of physics-inspired architectures designed to mitigate oversmoothing and vanishing gradient problems in deep GNNs. I…

cs.LG2025

Are We Measuring Oversmoothing in Graph Neural Networks Correctly?

Kaicheng Zhang, Piero Deidda, Desmond Higham +1

Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drop…

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