An Information-Theoretic Analysis of Temporal GNNs
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910562930130944 |
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| author | Farzaneh, Amirmohammad |
| author_facet | Farzaneh, Amirmohammad |
| contents | Temporal Graph Neural Networks, a new and trending area of machine learning, suffers from a lack of formal analysis. In this paper, information theory is used as the primary tool to provide a framework for the analysis of temporal GNNs. For this reason, the concept of information bottleneck is used and adjusted to be suitable for a temporal analysis of such networks. To this end, a new definition for Mutual Information Rate is provided, and the potential use of this new metric in the analysis of temporal GNNs is studied. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05624 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Information-Theoretic Analysis of Temporal GNNs Farzaneh, Amirmohammad Information Theory Machine Learning Temporal Graph Neural Networks, a new and trending area of machine learning, suffers from a lack of formal analysis. In this paper, information theory is used as the primary tool to provide a framework for the analysis of temporal GNNs. For this reason, the concept of information bottleneck is used and adjusted to be suitable for a temporal analysis of such networks. To this end, a new definition for Mutual Information Rate is provided, and the potential use of this new metric in the analysis of temporal GNNs is studied. |
| title | An Information-Theoretic Analysis of Temporal GNNs |
| topic | Information Theory Machine Learning |
| url | https://arxiv.org/abs/2408.05624 |