Inferring social network structure in ecological systems from spatio-temporal data streams
Open accessAbstract
We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these 'gathering events'. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix) is not given a priori. We perform experiments on two large-scale datasets (greater than 10(6) points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents.
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Based on Psorakis et al. (2012), the workflow converts a long series of time stamps into a social network.
“Based on the work of Psorakis et al. (2012), Fig. 4a summarizes the workflow used to convert a long series of time stamps into a social network.”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Psorakis et al. (2012)
Machine learning clustering methods for social affiliation appear to be robust and capture real-life pair bonds well.
“Such methods appear to be robust and capture real-life pair bonds well (Psorakis, Roberts, Rezek, & Sheldon, 2012).”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Psorakis et al., 2012
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Applications of machine learning in animal behaviour studies
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“For illustrative purposes, we focus on 4 weeks of data from 141 individuals visiting a single feeder located near Stithians, Cornwall, U.K. Based on the work of Psorakis et al. (2012), Fig.”
InfluentialInferring social structure from temporal data
Psorakis, I., Voelkl, B., Garroway, C. J., Radersma, R., Aplin, L. M., Crates, R. A., Culina, A., Farine, D. R., Firth, J. A., Hinde, C. A., et al. (2015) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-015-1906-0 · Cited by 95 · see citing works
Self-citation
- Authors
- Psorakis, I., Roberts, S. J., Rezek, I., & Sheldon, B. C.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Psorakis, Ioannis
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2012-06-13
- Volume
- 9
- Issue
- 76
- Pages
- 3055-3066
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- PMC
- PMC3479900
- OpenAlex
- W2099888920
- Cited by
- 171
- References
- 32
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 11 · 2023: 13 · 2022: 9 · 2021: 12 · 2020: 16 · 2019: 13 · 2018: 13 · 2017: 12 · 2016: 13 · 2015: 22 · 2014: 9 · 2013: 7 · 2012: 2
- FWCI
- 9.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of The Royal Society Interface (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/I011587/1)
- Natural Environment Research Council (NE/D011744/1)
- FP7 Ideas: European Research Council (250164)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Behavior and Reproduction
- Physical Sciences › Environmental Science › Ecology › Avian ecology and behavior
- Keywords
- social network inference, spatio-temporal data streams, co-occurrence networks, Parus major, bird foraging behavior, pair bonding, animal social networks, ecosystems
- MeSH terms
- Animals, England, Models, Theoretical, Pair Bond, Social Behavior, Spatial Behavior, Time Factors, Ecosystem, Passeriformes
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