Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Automated Data
2.2. SNA
3. Results
3.1. Group-Level SNA Traits
3.2. Communities and Cliques
3.3. Individual SNA Traits
4. Discussion
4.1. Group-Level SNA
4.2. Communities and Cliques
4.3. Individual SNA Traits
4.4. Implications for Management and Breeding
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Measure | Definition |
---|---|
Proximity | Proximity was defined by a threshold of 0.5 m Euclidean distance between the shoulders of the “standing” pigs, which was sustained for a duration longer than the average interaction time between each pair of individuals within each pen each day. |
Weighted SNA graph | A representation of a network where the edges (interactions) between nodes (animals) are assigned numerical values (weights) that reflect the duration of the interactions (proximity). |
Individual degree centrality | The number of edges (i.e., interactions) attached to a node (animal). |
Individual betweenness centrality | Betweenness centrality in a weighted network represents how often a node (or individual) acts as a bridge along the shortest paths between other nodes. |
Individual closeness centrality | The sum of the direct connections between a focal node and other nodes in the network. |
Individual eigenvector centrality | The connectivity of a node according to the all-degree centrality of the node and the all-degree centrality of the nodes that it connects with [16,38]. |
Individual clustering coefficient | The proportion of an individual node’s connections that are also connected with each other relative to the number of theoretically possible connections [10]. |
Centralization | A graph-level centralization is computed from the individual centrality scores of the nodes using the formula where Cmax is the centrality of the most central node. Ci is the centrality of the node, and the centralization was normalized by dividing by the maximum theoretical score for a graph with the same number of nodes [39]. For degree, closeness and betweenness centralization, the most centralized structure is a network where a small number of nodes hold most of the connections. For eigenvector centralization, a high centralization graph indicates that a small number of nodes have a very high eigenvector centrality, while the majority have much lower values. |
Density | The ratio of the number of edges of the network relative to the total number of possible edges in a group of the same size. |
Modularity | The strength of the division of a network into communities. It evaluates how well the network is divided into subgroups, where nodes within the same subgroup (or community) are more densely connected to each other than to nodes in other groups [40]. |
Community detection | The process of identifying subgroups or clusters of nodes within a network that are more densely connected internally compared to the connections in the network [40]. |
Modularity-based community detections method | The modularity-based method, using the Louvain algorithm for community detections, aims to maximize the modularity score to identify densely connected subgroups within the network [41]. |
Clique | A subset of nodes where every node is directly connected to every other node in the subset. |
Maximal clique | A clique is maximal if there is no node in the graph that can be added to this clique to create a larger clique without violating the clique property [42]. |
Co-membership | The relationship between nodes based on shared membership in specific cliques. |
Largest clique size | A clique with the maximum number of nodes among all cliques in that graph [42]. |
Pen | Period | Group Degree | Group Closeness | Group Eigenvector | Group Betweenness | Density | Modularity | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean (SD) | Max | Min | Mean (SD) | Max | Min | Mean (SD) | Max | Min | Mean (SD) | Max | Min | Mean (SD) | Max | Min | Mean (SD) | Max | Min | ||
1 | 1 | 0.36 (0.08) | 0.44 | 0.28 | 0.30 (0.27) | 0.50 | 0.00 | 0.48 (0.06) | 0.53 | 0.41 | 0.12 (0.07) | 0.18 | 0.04 | 0.40 (0.04) | 0.44 | 0.36 | 0.15 (0.06) | 0.22 | 0.10 |
1 | 2 | 0.37 (0.08) | 0.47 | 0.32 | 0.42 (0.12) | 0.56 | 0.35 | 0.45 (0.01) | 0.45 | 0.45 | 0.18 (0.05) | 0.24 | 0.14 | 0.40 (0.02) | 0.42 | 0.39 | 0.12 (0.04) | 0.15 | 0.08 |
2 | 1 | 0.25 (0.06) | 0.32 | 0.20 | 0.29 (0.09) | 0.39 | 0.23 | 0.37 (0.05) | 0.42 | 0.33 | 0.09 (0.01) | 0.10 | 0.08 | 0.44 (0.02) | 0.46 | 0.42 | 0.19 (0.02) | 0.20 | 0.17 |
2 | 2 | 0.3 (0.13) | 0.45 | 0.22 | 0.35 (0.18) | 0.55 | 0.23 | 0.42 (0.04) | 0.46 | 0.39 | 0.14 (0.1) | 0.25 | 0.08 | 0.42 (0.01) | 0.43 | 0.41 | 0.14 (0.03) | 0.17 | 0.11 |
3 | 1 | 0.31 (0.1) | 0.41 | 0.22 | 0.34 (0.12) | 0.48 | 0.24 | 0.40 (0.07) | 0.47 | 0.33 | 0.12 (0.05) | 0.18 | 0.08 | 0.42 (0.01) | 0.42 | 0.42 | 0.15 (0.03) | 0.18 | 0.12 |
3 | 2 | 0.26 (0.09) | 0.36 | 0.20 | 0.32 (0.11) | 0.45 | 0.24 | 0.43 (0.04) | 0.46 | 0.39 | 0.11 (0.04) | 0.15 | 0.08 | 0.41 (0.03) | 0.44 | 0.38 | 0.18 (0.02) | 0.19 | 0.16 |
4 | 1 | 0.31 (0.03) | 0.34 | 0.28 | 0.13 (0.22) | 0.38 | 0.00 | 0.41 (0.02) | 0.43 | 0.40 | 0.10 (0.06) | 0.16 | 0.04 | 0.44 (0.01) | 0.44 | 0.43 | 0.13 (0.04) | 0.17 | 0.10 |
4 | 2 | 0.37 (0.04) | 0.40 | 0.33 | 0.45 (0.02) | 0.46 | 0.43 | 0.45 (0.03) | 0.47 | 0.41 | 0.16 (0.03) | 0.18 | 0.12 | 0.44 (0.04) | 0.48 | 0.41 | 0.14 (0.03) | 0.17 | 0.12 |
5 | 1 | 0.27 (0.01) | 0.28 | 0.26 | 0.30 (0.03) | 0.32 | 0.27 | 0.42 (0.03) | 0.46 | 0.40 | 0.09 (0.02) | 0.10 | 0.07 | 0.41 (0.03) | 0.44 | 0.39 | 0.18 (0.01) | 0.18 | 0.17 |
5 | 2 | 0.39 (0.01) | 0.40 | 0.38 | 0.45 (0.01) | 0.46 | 0.44 | 0.47 (0.05) | 0.51 | 0.42 | 0.14 (0.04) | 0.18 | 0.10 | 0.41 (0.02) | 0.43 | 0.39 | 0.15 (0.03) | 0.17 | 0.12 |
6 | 1 | 0.33 (0.08) | 0.42 | 0.26 | 0.37 (0.11) | 0.49 | 0.29 | 0.43 (0.07) | 0.50 | 0.36 | 0.10 (0.05) | 0.15 | 0.06 | 0.44 (0.02) | 0.46 | 0.42 | 0.14 (0.02) | 0.15 | 0.12 |
6 | 2 | 0.38 (0.04) | 0.42 | 0.34 | 0.47 (0.06) | 0.54 | 0.42 | 0.45 (0.01) | 0.45 | 0.44 | 0.16 (0.03) | 0.18 | 0.12 | 0.44 (0.02) | 0.46 | 0.42 | 0.16 (0.06) | 0.22 | 0.11 |
Pen | Period | No. of Communities | No. of Maximal Cliques | Largest Clique Size | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Mean (SD) | Max | Min | Mean (SD) | Max | Min | Mean (SD) | Max | Min | ||
1 | 1 | 4.0 (1.0) | 5.0 | 3.0 | 27.7 (4.7) | 33.0 | 24.0 | 5.3 (0.6) | 6.0 | 5.0 |
2 | 3.0 (0.0) | 3.0 | 3.0 | 20.0 (6.2) | 25.0 | 13.0 | 6.7 (1.2) | 8.0 | 6.0 | |
2 | 1 | 3.0 (1.00) | 4.0 | 2.0 | 29.0 (2.0) | 31.0 | 27.0 | 5.7 (0.6) | 6.0 | 5.0 |
2 | 3.7 (0.60) | 4.0 | 3.0 | 26.0 (7.2) | 34.0 | 20.0 | 5.3 (0.6) | 6.0 | 5.0 | |
3 | 1 | 3.3 (0.60) | 4.0 | 3.0 | 24.0 (2.0) | 26.0 | 22.0 | 5.3 (0.6) | 6.0 | 5.0 |
2 | 3.3 (0.60) | 4.0 | 3.0 | 16.0 (5.3) | 22.0 | 12.0 | 5.3 (1.2) | 6.0 | 4.0 | |
4 | 1 | 4.0 (0) | 4.0 | 4.0 | 24.3 (3.1) | 27.0 | 21.0 | 6.3 (0.6) | 7.0 | 6.0 |
2 | 3.0 (0) | 3.0 | 3.0 | 24.7 (2.1) | 27.0 | 23.0 | 5.0 (1.0) | 6.0 | 4.0 | |
5 | 1 | 3.7 (0.6) | 4.0 | 3.0 | 32.0 (3.5) | 34.0 | 28.0 | 5.3 (0.6) | 6.0 | 5.0 |
2 | 3.7 (0.6) | 4.0 | 3.0 | 25.3 (2.5) | 28.0 | 23.0 | 5.7 (0.6) | 6.0 | 5.0 | |
6 | 1 | 3.0 (0) | 3.0 | 3.0 | 33.7 (2.5) | 36.0 | 31.0 | 5.3 (0.6) | 6.0 | 5.0 |
2 | 3.3 (0.6) | 4.0 | 3.0 | 20.0 (2.0) | 22.0 | 18.0 | 6.7 (0.6) | 7.0 | 6.0 |
Trait | Early Growing Period | Later Growing Period | ||||
---|---|---|---|---|---|---|
Mean (SD) | Max | Min | Mean (SD) | Max | Min | |
Individual degree centrality | 0.42 (0.16) | 0.83 | 0 | 0.42 (0.18) | 0.90 | 0.06 |
Individual closeness centrality | 0.02 (0.001) | 0.03 | 0 | 0.03 (0.01) | 0.05 | 0.01 |
Individual betweenness centrality | 0.04 (0.04) | 0.23 | 0 | 0.04 (0.05) | 0.29 | 0.00 |
Individual eigenvector centrality | 0.56 (0.24) | 1.00 | 0 | 0.53 (0.26) | 1.00 | 0.04 |
Individual clustering coefficient | 0.55 (0.17) | 1.00 | 0 | 0.60 (0.20) | 1.00 | 0.00 |
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Agha, S.; Psota, E.; Turner, S.P.; Lewis, C.R.G.; Steibel, J.P.; Doeschl-Wilson, A. Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis. Animals 2025, 15, 996. https://doi.org/10.3390/ani15070996
Agha S, Psota E, Turner SP, Lewis CRG, Steibel JP, Doeschl-Wilson A. Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis. Animals. 2025; 15(7):996. https://doi.org/10.3390/ani15070996
Chicago/Turabian StyleAgha, Saif, Eric Psota, Simon P. Turner, Craig R. G. Lewis, Juan Pedro Steibel, and Andrea Doeschl-Wilson. 2025. "Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis" Animals 15, no. 7: 996. https://doi.org/10.3390/ani15070996
APA StyleAgha, S., Psota, E., Turner, S. P., Lewis, C. R. G., Steibel, J. P., & Doeschl-Wilson, A. (2025). Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis. Animals, 15(7), 996. https://doi.org/10.3390/ani15070996