Random Walk

Random walks, fundamental stochastic processes modeling movement through a network or space, are being actively researched for their applications in diverse fields. Current research focuses on improving the efficiency and accuracy of random walk algorithms, particularly in the context of graph neural networks and diffusion models, with advancements in techniques like Metropolis-Hastings with Lévy jumps and self-repelling random walks addressing limitations in convergence and exploration. These improvements have significant implications for various applications, including link prediction, anomaly detection, decentralized learning, and even drug repurposing, by enabling more efficient and accurate analysis of complex systems.