ArticleAnimals : an open access journal from MDPI2019
Agonistic Interactions in Pigs-Comparison of Dominance Indices with Parameters Derived from Social Network Analysis in Three Age Groups.
Article in Animals : an open access journal from MDPI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Systematic review of animal-based indicators to measure thermal, social, and immune-related stress in pigs.PloS one · 2022Pooled it
- Understanding the Impact of Social Stress on Serum Metabolome and Saliva Biomarkers in Growing-Finishing Pigs.Animals : an open access journal from MDPI · 2025Article
- Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis.Animals : an open access journal from MDPI · 2025Article
- Impact of proximity definitions and sampling rates on social networks in pigs based on tracking using computer vision.Scientific reports · 2025Article
- When the neighbors are noisy: effect of social challenge in collateral pens of stressed animals.Frontiers in veterinary science · 2024Article
- Evaluating social network metrics as indicators of tail injury caused by tail biting in growing-finishing pigs (Frontiers in veterinary science · 2024Article
- Social Network Analysis in Farm Animals: Sensor-Based Approaches.Animals : an open access journal from MDPI · 2021Review
- Using Flies to Understand Social Networks.Frontiers in neural circuits · 2021Review
- Social Network Analysis of Agonistic Behaviour and Its Association with Economically Important Traits in Pigs.Animals : an open access journal from MDPI · 2020Article
- "HerdGPS-Preprocessor"-A Tool to Preprocess Herd Animal GPS Data; Applied to Evaluate Contact Structures in Loose-Housing Horses.Animals : an open access journal from MDPI · 2020Article
- Prospects for the Analysis and Reduction of Damaging Behaviour in Group-Housed Livestock, With Application to Pig Breeding.Frontiers in genetics · 2020Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dominance indices are often calculated using the number of won and lost fights of each animal focusing on dyadic interactions. Social network analysis provides new insights into the establishment of stable group structures going beyond the dyadic approach. Thus, it was investigated whether centrality parameters describing the importance of each animal for the network are able to capture the rank order calculated by dominance indices. Therefore, two dominance indices and five centrality parameters based on two network types (initiator-receiver and winner-loser networks) were calculated regarding agonistic interactions observed in three mixing events (weaned piglets, fattening pigs, gilts). Comparing the two network types, the winner-loser networks demonstrated highly positive correlation coefficients between out-degree and outgoing closeness and the dominance indices. These results were confirmed by partial least squares structural equation modelling (PLS-SEM), i.e., about 60% of the variance of the dominance could be explained by the centrality parameters, whereby the winner-loser networks could better illustrate the dominance hierarchy with path coefficients of about 1.1 for all age groups. Thus, centrality parameters can portray the dominance hierarchy providing more detailed insights into group structure which goes beyond the dyadic approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.