An Information-Theoretic Analysis of Temporal GNNs
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Abstract (arXiv)
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.
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References · 5
- 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
- 2017Opening the Black Box of Deep Neural Networks via Informationcited by 503
- 2019Representation Learning for Dynamic Graphs: A Surveycited by 265
- 2020The Information Bottleneck Problem and Its Applications in Machine Learningcited by 66
- 2022An Information Theory Approach to Network Evolution Modelscited by 1
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