Community detection in networks without observing edges
| العنوان: | Community detection in networks without observing edges |
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| المؤلفون: | Nick S. Jones, Renaud Lambiotte, Leto Peel, Till Hoffmann |
| المساهمون: | Engineering & Physical Science Research Council (EPSRC), UCL - SST/ICTM - Institute of Information and Communication Technologies, Electronics and Applied Mathematics |
| المصدر: | Science advances, 6(4):1478. American Association for the Advancement of Science Science Advances Science Advances, Vol. 6, no.4 (2020) |
| بيانات النشر: | American Association for the Advancement of Science (AAAS), 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer Science - Machine Learning, Physics - Physics and Society, Scale (ratio), Computer science, cs.LG, FOS: Physical sciences, Physics and Society (physics.soc-ph), 02 engineering and technology, computer.software_genre, 01 natural sciences, Machine Learning (cs.LG), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Bayesian hierarchical modeling, Point estimation, 010306 general physics, Research Articles, Selection (genetic algorithm), Social and Information Networks (cs.SI), Network Science, Sequence, Science & Technology, Multidisciplinary, Series (mathematics), physics.soc-ph, SciAdv r-articles, MIXTURES, Computer Science - Social and Information Networks, Multidisciplinary Sciences, MODEL, Science & Technology - Other Topics, 020201 artificial intelligence & image processing, Data mining, cs.SI, VARIATIONAL BAYESIAN-INFERENCE, computer, Research Article |
| الوصف: | We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index as well as climate data from US cities. 16 pages, 7 figures |
| تدمد: | 2375-2548 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14bbee71de979a363df0a9493148c0ceTest https://doi.org/10.1126/sciadv.aav1478Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....14bbee71de979a363df0a9493148c0ce |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23752548 |
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