Analysis of Dirichlet Energies as Over-smoothing Measures
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915666688212992 |
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| author | Bison, Anna Sperduti, Alessandro |
| author_facet | Bison, Anna Sperduti, Alessandro |
| contents | 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 \textit{et 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analysis of Dirichlet Energies as Over-smoothing Measures Bison, Anna Sperduti, Alessandro Machine Learning 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 \textit{et 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. |
| title | Analysis of Dirichlet Energies as Over-smoothing Measures |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.09890 |