An Information-Theoretic Analysis of Temporal GNNs

Amirmohammad Farzaneh

An Information-Theoretic Analysis of Temporal GNNs

Information Theory 2024-08-13 v1 Machine Learning math.IT

Authors: Amirmohammad Farzaneh

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.

Keywords

temporal graph information theory graph neural network

Cite

@article{arxiv.2408.05624,
  title  = {An Information-Theoretic Analysis of Temporal GNNs},
  author = {Amirmohammad Farzaneh},
  journal= {arXiv preprint arXiv:2408.05624},
  year   = {2024}
}

Comments

To be presented at Information Theory Workshop 2024

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