An Information-Theoretic Analysis of Temporal GNNs
<|reference_start|>An Information-Theoretic Analysis of Temporal GNNs: 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.<|reference_end|>
@article{farzaneh2024an,
    title={An Information-Theoretic Analysis of Temporal GNNs},
    author={Amirmohammad Farzaneh},
    journal={arXiv preprint arXiv:2408.05624},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05624},
    primaryClass={cs.IT cs.LG math.IT}
}