An Information-Theoretic Analysis on Temporal Graph Evolution

Published: 20 Oct 2023, Last Modified: 14 Nov 2023TGL Workshop 2023 LongPaperEveryoneRevisionsBibTeX
Keywords: Information Theory, Graph Evolution, Entropy Rate
TL;DR: The paper proposes an information-theoretic model to simulate the evolution of dynamic networks with potential applications to graph neural networks.
Abstract: In this paper, we present a novel model termed Network Evolution Chains for simulating the temporal dynamics of networks. Our model's design is tailored to enable comprehensive analysis through information theory. We establish that this model creates a stationary and ergodic stochastic process, thus facilitating the application of the asymptotic equipartition property. This breakthrough paves the way for a thorough information-theoretic investigation into network behavior, encompassing the definition of typical sequences, future state prediction, and beyond.
Format: Long paper, up to 8 pages. If the reviewers recommend it to be changed to a short paper, I would be willing to revise my paper to fit within 4 pages.
Submission Number: 7