The content discusses disentangling timescales in complex systems using a Bayesian approach. It introduces methodologies for change point detection and spectrum analysis, showcasing applications on synthetic and empirical datasets like Enron and OSS projects.
Changes in system dynamics can serve as early-warning signals for critical transitions or disruptions. The study focuses on identifying shifts in system dynamics and inferring the distribution of timescales to enhance understanding of complex systems' behavior.
Key systemic properties like resilience depend on processes operating across various timescales, from immediate events to long-term evolutions. The study proposes a dual approach involving change point detection and spectrum analysis to analyze temporal networks comprehensively.
Temporal network models map interactions to sequences of events, providing insights into temporal dynamics. Existing methods excel at tasks like community detection but fall short in capturing the full spectrum of timescales governing complex systems.
The authors propose a Bayesian approach that goes beyond identifying change points to infer the spectrum of timescales driving network dynamics. This methodology enhances understanding of complex systems by unraveling processes occurring at different timescales within a system.
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by Giona Casira... às arxiv.org 03-11-2024
https://arxiv.org/pdf/2403.05343.pdfPerguntas Mais Profundas