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A continuum limit for the PageRank algorithm
[article]
2021
arXiv
pre-print
Semi-supervised and unsupervised machine learning methods often rely on graphs to model data, prompting research on how theoretical properties of operators on graphs are leveraged in learning problems. While most of the existing literature focuses on undirected graphs, directed graphs are very important in practice, giving models for physical, biological, or transportation networks, among many other applications. In this paper, we propose a new framework for rigorously studying continuum limits
arXiv:2001.08973v3
fatcat:g5rlh2pxkfgohoaeie7nz77e7e