Exposure theory for learning complex networks with random walks
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Publication:5881461
DOI10.1093/COMNET/CNAC029zbMATH Open1506.68096arXiv2202.11262OpenAlexW4293878753MaRDI QIDQ5881461
Author name not available (Why is that?)
Publication date: 10 March 2023
Published in: (Search for Journal in Brave)
Abstract: Random walks are a common model for exploration and discovery of complex networks. While numerous algorithms have been proposed to map out an unknown network, a complementary question arises: in a known network, which nodes and edges are most likely to be discovered by a random walker in finite time? Here we introduce exposure theory, a statistical mechanics framework that predicts the learning of nodes and edges across several types of networks, including weighted and temporal, and show that edge learning follows a universal trajectory. While the learning of individual nodes and edges is noisy, exposure theory produces a highly accurate prediction of aggregate exploration statistics.
Full work available at URL: https://arxiv.org/abs/2202.11262
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