The stability of social prominence and influence in a dynamic sow herd: A social network analysis approach

Jowett, Sarah; Amory, Jonathan

doi:10.1016/j.applanim.2021.105320

Cited by 12 publications

(7 citation statements)

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“…The blue squares represent the individual nodes within the network. Previously published in Jowett and Amory ( 45 ). Copyrights were obtained.…”

Section: Resultsmentioning

confidence: 99%

“…Our previous social network analysis study investigated preferential associations to identify prominent and influential sows in three preferential association networks ( 45 ). The findings revealed subgroups ( k- cores) with an assortment of connectedness and general instability in behavior.…”

Section: Introductionmentioning

confidence: 99%

“…In the current study, these social mechanisms are determined by the application of subgroups, including the k-cores (connectedness) and brokering typologies. Traditionally, brokering behavior is determined with the application of the social network metric of betweenness centrality, which extends beyond dyadic interactions to identify individuals within a social group that may be more influential than conspecifics in a behavioral or biological transmission network (44)(45)(46). In affiliative behavior networks, brokering individuals that broker are important for group fitness as they allow for increased social cohesion [e.g., (47)].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preferential associations in an unstable social network: applying social network analysis to a dynamic sow herd

2023

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Preferential associations are fitness-enhancing ties between individuals, documented in a range of taxa. Despite this, research into preferential associations remains underrepresented in commercial species, particularly pigs. This study investigates the development of preferential associations in a dynamic sow herd. Preferential associations were defined as approaching a resting sow and then sitting or lying with physical contact with the selected sow, separated by < 1 m from the head or directly next to her, with interaction tolerated for > 60 s. For individual identification, each sow was marked with colored dots, stripes, or both, corresponding to their ear-tag number. Preferential associations were measured over one production cycle of 21 days. Behavioral observations took place on 7 days of the study, with 3 h of behavior per day recorded during peak activity times (08:00–09:00, 15:00–16:00, 20:00–21:00 h). Behaviors were recorded using five cameras, each positioned within the barn to provide coverage of the functional areas. The network metrics applied included in-degree centrality (received ties), out-degree centrality (initiated ties), centralization (the extent to which an individual is central within the network), clustering coefficient (a measure of tie strength), and the E-I Index (a measure of assortment by trait: parity, familiarity, and sociality). Individuals were added and removed during the study, so the centrality metrics of missing sows were weighted. To describe the structure of the network, brokerage typologies were applied. Brokerage typologies include five positions, including coordinators, gatekeepers, representatives, consultants, and liaisons. The results revealed social discrimination in assortment by connectedness even when ties were not reciprocal, and the most connected sows were significantly more likely to be approached than less connected individuals. The most connected sows had significantly higher in-degree and out-degree centrality. With the application of brokerage typologies, the results showed a relationship between connectedness and brokering type, with the most connected sows predominantly engaging in coordinating behavior. The results suggest that the motivation for discrimination in the unstable preferential association network was not founded upon bidirectional interactions. These findings highlight the complexities involved when forming social preferences and present a platform for further exploring the motivations for preferential associations among intensively farmed pigs.

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“…The blue squares represent the individual nodes within the network. Previously published in Jowett and Amory ( 45 ). Copyrights were obtained.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preferential associations in an unstable social network: applying social network analysis to a dynamic sow herd

2023

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“…The PageRank algorithm introduces a concept called random viewer, which assumes that a user keeps clicking on external links within a web page until there are no more external links within the web page accessed through these links, at which point he closes the original web page and randomly browses other web pages, which can be unrelated to the previous web page. To deal with the problem posed by pages that have no external links, the PageRank algorithm assumes that such pages link to all pages in the set in order to deal with the case where the probability drops to 0 due to the absence of subsequent pages, and the PageRank value of such pages will be divided equally among all pages, expressed in the original equation with n as the denominator [18].…”

Section: Based On Ranking Algorithmmentioning

confidence: 99%

Research on the effect of animation IP dissemination based on social network analysis

Zheng

2023

Applied Mathematics and Nonlinear Sciences

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This paper constructs a GCN-based animation IP propagation algorithm based on social network analysis. Through the analysis of social network influence, a topology-based social network influence model is constructed. Combined with the ranking algorithm, the influence size of animation IP nodes is judged. The LDA model was used to construct a huge document set with the character IPs in the animation as documents. The clustering algorithm is used to classify the propagation effect of animation IP nodes, and the interval of animation IP propagation maximization is found by combining the submodular function maximization. The feature extraction of animation IP propagation effect maximization is performed by graph convolutional neural network. Evaluation indexes are constructed to assess the spreading ability of animation IP under social networks. The results show that the spreading power of animation IP with negative celebrity endorsement is −0.1, and the spreading effect of animation IP with positive communication content is 0.6.

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“…Social network analysis (SNA) is a normative approach used to reveal the social relationships, structure, and resource fluidity between different actors within a network (Scott, 1988). It is used extensively in the social and behavioral sciences, including in the virtual community (Can & Alatas, 2019; Watanabe et al, 2021), the biological field (Jowett & Amory, 2021; Suyo et al, 2020), the medical field (De Rosis et al, 2021; Pierce et al, 2021), tourism areas (Gan et al, 2021; Restrepo et al, 2021), economic areas (Yuan et al, 2021; Zhong et al, 2021), and the ecological field (Chen et al, 2020; Ghafoori Kharanagh et al, 2020). SNA has been applied in the field of industrial ecology (Domenech & Davies, 2011; Song et al, 2018) because it can facilitate the operation of industrial symbiosis networks by combining the impact of both economic and environmental aspects (Chertow, 2007; Doménech & Davies, 2009; Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Measuring structural characteristics and evolutionary patterns of an industrial carbon footprint network: A social network analysis approach

Yuan

Sun

Liu³

2022

Regional Science Policy & Practice

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The formation of an industrial network is inevitable because an industrial structure evolves to a higher level. The continuous material exchange among sectors creates a path for the embodied carbon in products to flow freely among industries. With the gradual complication of industrial networks, however, the emission reduction behavior of sectors not only becomes rooted in individual conduct, but also originates from the interaction forces between individuals. Therefore, carbon management in industries in the real economy needs to be explored from the perspective of these complex networks. On the basis of these considerations, this study utilized a hybrid model, the Economic Input–Output Life Cycle Assessment model, to construct the carbon footprint network of China's industries in 2002, 2007, 2012, and 2017. We also applied social network analysis to quantify the structural characteristics and evolutionary patterns of the network. Results showed that the transmission efficiency of resources and the subgroup‐integrated degree of the actors within the carbon footprint network were excellent, which was conducive to coordinated carbon emission reduction of these sectors from a network perspective. Agriculture and services were the engines driving sector‐wide carbon emission reduction through links within the network. Furthermore, some sectors, such as transportation and the warehousing industry, basic chemicals and chemical products manufacturing, and metal smelting and rolling processing industry, act as bridges and brokers. These sectors are keys to ensuring network cohesion and reducing the risk of network fragmentation. Finally, the industrial block network presented a hierarchical structure of a core–edge network. The network followed a trend of structural changes in which the network core became increasingly clear, and the network distribution pattern gradually concentrated on a single block. This finding revealed a new pathway for collaborative carbon mitigation in the industry from the perspective of the industrial community.

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The stability of social prominence and influence in a dynamic sow herd: A social network analysis approach

Cited by 12 publications

References 80 publications

Preferential associations in an unstable social network: applying social network analysis to a dynamic sow herd

Preferential associations in an unstable social network: applying social network analysis to a dynamic sow herd

Research on the effect of animation IP dissemination based on social network analysis

Measuring structural characteristics and evolutionary patterns of an industrial carbon footprint network: A social network analysis approach

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