mlsanityData to 14 Oct 2026

Analysis of Dirichlet Energies as Over-smoothing Measures

arXiv 2512.09890 ↗First posted 10 Dec 2025cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

We analyze the distinctions between two functionals often used as over-smoothing measures: the Dirichlet energies induced by the unnormalized graph Laplacian and the normalized graph Laplacian. We demonstrate that the latter fails to satisfy the axiomatic definition of a node-similarity measure proposed by Rusch textitet al. By formalizing fundamental spectral properties of these two definitions, we highlight critical distinctions necessary to select the metric that is spectrally compatible with the GNN architecture, thereby resolving ambiguities in monitoring the dynamics.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 10 Dec 2025.

Builds on

7 of 10 references.

References · 7

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018How Powerful are Graph Neural Networks?cited by 4,393
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  4. 2020A Note on Over-Smoothing for Graph Neural Networkscited by 249
  5. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 247
  6. 2025On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learningcited by 43
  7. 2025A Spectral Interpretation of Redundancy in a Graph Reservoircited by 1

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