Identifying the latent space geometry of network models through analysis of curvature
| العنوان: | Identifying the latent space geometry of network models through analysis of curvature |
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| المؤلفون: | Shane Lubold, Arun G. Chandrasekhar, Tyler McCormick |
| المصدر: | Journal of the Royal Statistical Society Series B: Statistical Methodology. 85:240-292 |
| بيانات النشر: | Oxford University Press (OUP), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Social and Information Networks (cs.SI), FOS: Computer and information sciences, Statistics and Probability, Geometric Topology (math.GT), Machine Learning (stat.ML), Computer Science - Social and Information Networks, Statistics - Applications, Methodology (stat.ME), Mathematics - Geometric Topology, Statistics - Machine Learning, FOS: Mathematics, Applications (stat.AP), Statistics, Probability and Uncertainty, Statistics - Methodology |
| الوصف: | A common approach to modelling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection likelihood. More positive manifold curvature encourages more and tighter communities; negative curvature induces repulsion. We consistently estimate manifold type, dimension, and curvature from simply connected, complete Riemannian manifolds of constant curvature. We represent the graph as a noisy distance matrix based on the ties between cliques, then develop hypothesis tests to determine whether the observed distances could plausibly be embedded isometrically in each of the candidate geometries. We apply our approach to datasets from economics and neuroscience. |
| تدمد: | 1467-9868 1369-7412 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bde91e49eb702175afa6590a6f6d340Test https://doi.org/10.1093/jrsssb/qkad002Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....6bde91e49eb702175afa6590a6f6d340 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14679868 13697412 |
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