AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is argued that continuous-time graph learning should identify low-dimensional states sufficient for future prediction rather than reconstruct the full history, and proposed PreDyG, a lightweight model whose capacity is matched to the intrinsic predictive dimension.
STHMoE decouples traffic dynamics into frequency-domain, time-domain, spatio-domain, spatio-domain, and higher-order spatial representations, which are modeled by prompt-guided heterogeneous experts built upon a partially frozen LLM backbone.
This review surveys system identification for complex dynamical systems through a unified framework that connects model structure, operating mode, estimation target, and computational setting and identifies key open challenges, including robust identification under biased or limited data, uncertainty-aware modeling, in...
A unified statistical physics framework based on the random cluster model is introduced that encompasses classical notions of connectivity and defines a continuous family of new connectivity measures, offering a powerful tool to analyze structure and dynamics in complex networks.
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