Dynamic social networks based on movement
Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of th...
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ftdatacite:10.48550/arxiv.1512.07607 2023-05-15T17:03:35+02:00 Dynamic social networks based on movement Scharf, Henry R. Hooten, Mevin B. Fosdick, Bailey K. Johnson, Devin S. London, Josh M. Durban, John W. 2015 https://dx.doi.org/10.48550/arxiv.1512.07607 https://arxiv.org/abs/1512.07607 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Applications stat.AP Methodology stat.ME FOS Computer and information sciences Preprint Article article CreativeWork 2015 ftdatacite https://doi.org/10.48550/arxiv.1512.07607 2022-04-01T11:47:27Z Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus telemetry data, which are minimally-invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect, and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge. Report Killer Whale Killer whale DataCite Metadata Store (German National Library of Science and Technology) |
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Applications stat.AP Methodology stat.ME FOS Computer and information sciences |
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Applications stat.AP Methodology stat.ME FOS Computer and information sciences Scharf, Henry R. Hooten, Mevin B. Fosdick, Bailey K. Johnson, Devin S. London, Josh M. Durban, John W. Dynamic social networks based on movement |
topic_facet |
Applications stat.AP Methodology stat.ME FOS Computer and information sciences |
description |
Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus telemetry data, which are minimally-invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect, and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge. |
format |
Report |
author |
Scharf, Henry R. Hooten, Mevin B. Fosdick, Bailey K. Johnson, Devin S. London, Josh M. Durban, John W. |
author_facet |
Scharf, Henry R. Hooten, Mevin B. Fosdick, Bailey K. Johnson, Devin S. London, Josh M. Durban, John W. |
author_sort |
Scharf, Henry R. |
title |
Dynamic social networks based on movement |
title_short |
Dynamic social networks based on movement |
title_full |
Dynamic social networks based on movement |
title_fullStr |
Dynamic social networks based on movement |
title_full_unstemmed |
Dynamic social networks based on movement |
title_sort |
dynamic social networks based on movement |
publisher |
arXiv |
publishDate |
2015 |
url |
https://dx.doi.org/10.48550/arxiv.1512.07607 https://arxiv.org/abs/1512.07607 |
genre |
Killer Whale Killer whale |
genre_facet |
Killer Whale Killer whale |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1512.07607 |
_version_ |
1766057489762091008 |