arXiv:2605.21514v1 Announce Type: cross Abstract: Many complex systems can be modeled by temporal networks, whose organization often evolves through distinct structural phases. Detecting the change points that delimit these phases is both important and challenging. In this work, we extend the conditional entropy of heat diffusion from static graphs to temporal networks and study its properties. We provide an upper bound and explain how discrepancies from it arise from the presence of asymmetric temporal paths. Moreover, we show that this quantity is monotone in time, yielding an information-th
This research builds on existing work in complex systems and network science, applying advanced mathematical tools to better understand dynamic system behavior.
Improved methods for detecting structural phase changes in temporal networks can enhance our ability to predict and manage complex systems across various domains.
The proposed extension of conditional entropy to temporal networks offers a novel, monotone information-theoretic quantity for analyzing system evolution over time.
- · AI/ML researchers
- · Network scientists
- · Data analysts
- · Traditional static network analysis methods
Enhanced algorithms for change point detection in dynamic systems become available.
Improved predictive models for phenomena like financial market shifts, disease outbreaks, or infrastructure failures.
More resilient and adaptive AI systems capable of autonomously recognizing and responding to structural changes in real-world environments.
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Read at arXiv cs.LG