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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.<\/jats:p>","DOI":"10.1093\/comnet\/cnac029","type":"journal-article","created":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T22:08:05Z","timestamp":1661638085000},"source":"Crossref","is-referenced-by-count":2,"title":["Exposure theory for learning complex networks with random walks"],"prefix":"10.1093","volume":"10","author":[{"given":"Andrei A","family":"Klishin","sequence":"first","affiliation":[{"name":"Department of Bioengineering, University of Pennsylvania , 210 S. 33rd Street, 240 Skirkanich Hall, Philadelphia, PA 19104-6321, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dani S","family":"Bassett","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, Department of Physics & Astronomy , Department of Electrical & Systems Engineering, Department of Neurology and Department of Psychiatry, University of Pennsylvania, 210 S. 33rd Street, 240 Skirkanich Hall, Philadelphia, PA 19104-6321, USA and Santa Fe Institute, Santa Fe, NM 87501, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"key":"2022082722080224600_B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2017.07.007","article-title":"Random walks and diffusion on networks","volume":"716","author":"Masuda,","year":"2017","journal-title":"Phys. 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