arXiv:2503.06115v2 Announce Type: replace-cross Abstract: Reinforced random walks (RRWs), including vertex-reinforced random walks (VRRWs) and edge-reinforced random walks (ERRWs), model random walks where the transition probabilities evolve based on prior visitation history~\cite{mgr, fmk, tarres, volkov}. These models have found applications in various areas, such as network representation learning~\cite{xzzs}, reinforced PageRank~\cite{gly}, and modeling animal behaviors~\cite{smouse}, among others. However, statistical estimation of the parameters governing RRWs remains underexplored. This
This paper addresses a core statistical challenge in understanding complex adaptive systems, with renewed interest due to AI's impact on model complexity and data analysis.
Improved statistical estimation for reinforced random walks could significantly enhance the reliability and interpretability of AI models across various critical applications.
Better methods for parameter estimation in reinforced random walk models could lead to more robust and predictable AI systems, particularly in network analysis and learning.
- · AI/ML researchers
- · Network analysis software providers
- · Data scientists
- · Developers of adaptive AI systems
- · Organizations relying on simplistic, non-adaptive models
- · Researchers using heuristic approaches for reinforced random walk estimation
More accurate predictions and understanding of dynamic network processes and adaptive behaviors.
Development of new AI algorithms that can better learn and adapt from sequential data with reinforcement.
Enhanced AI systems capable of modeling and predicting complex phenomena like market dynamics or social contagion with greater precision.
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