An Information-Theoretic Analysis of Temporal GNNs

Amirmohammad Farzaneh

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arXiv · 2408.05624

An Information-Theoretic Analysis of Temporal GNNs

Amirmohammad Farzaneh

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Abstract

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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Amirmohammad Farzaneh. 2024-08-10. An Information-Theoretic Analysis of Temporal GNNs. https://arxiv.org/abs/2408.05624

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