Social network analysis - centrality parameters and individual network positions of agonistic behavior in pigs over three different age levels

Büttner, Kathrin; Scheffler, K.; Czycholl, Irena; Krieter, Joachim

doi:10.1186/s40064-015-0963-1

Cited by 31 publications

(33 citation statements)

References 44 publications

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“…Not only in molecular biology networks but also in all types of networks, finding the influential nodes is the chief question of centrality analysis (3). Examples include predicting the details of information controlling or disease spreading within a specific network in order to delineate how to effectively implement target marketing or preventive healthcare (4)(5)(6). Several centralities measures (mostly in the context of social network analyses) have been described (3) in the last decades.…”

Section: Introductionmentioning

confidence: 99%

A systematic survey of centrality measures for protein-protein interaction networks

Ashtiani

Salehzadeh

Razaghi-Moghadam

et al. 2017

Preprint

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Background: Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.

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Section: Introductionmentioning

confidence: 99%

A systematic survey of centrality measures for protein-protein interaction networks

Ashtiani

Salehzadeh

Razaghi-Moghadam

et al. 2017

Preprint

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“…The inclusion of insignificant dyads also increased the probability of forming larger connected components. Furthermore, the centrality parameters showed only moderate r S values indicating that the rank order of the animals based on these parameters was influenced by the exclusion of insignificant dyads, whereby higher r S values were obtained for out-degree and outgoing closeness, that is, centrality parameters reflecting an active behaviour, compared to in-degree and ingoing closeness, that is, centrality parameters reflecting a passive behaviour (Büttner et al, 2015a and2015b). It has to be borne in mind that all centrality parameters were measured for the network based on information of the initiator and the receiver of the fight without information about the further sequence of behavioural patterns.…”

Section: Social Network Analysismentioning

confidence: 99%

“…Here, the natural behaviour of the animals is influenced by environmental impacts made by humans, for example, limited space allowance or predetermined pen mates (Koene and Ipema, 2014). In the wild, a subordinate animal can more easily avoid an agonistic interaction compared to the artificial environment of a stable with few means of escape (Büttner et al, 2015b). Also other studies stated that with increased available space the amount of agonistic interactions is reduced (Remience et al, 2008;Hemsworth et al, 2013;Rault, 2017).…”

Section: Social Network Analysismentioning

confidence: 99%

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Social network analysis in pigs: impacts of significant dyads on general network and centrality parameters

Büttner

Czycholl

Mees

et al. 2020

Animal

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In general, one animal is considered dominant over another animal if it has won more fights than its opponent. Whether this difference in won and lost fights is significant is neglected in most studies. Thus, the present study evaluates the impact of two different calculation methods for dyadic interactions with a significant asymmetric outcome on the results of social network analysis regarding agonistic interactions of pigs in three different mixing events (weaned piglets, fattening pigs and gilts). Directly after mixing, all animals were video recorded for 17 (fattening pigs, gilts) and 28 h (weaned piglets), documenting agonistic interactions. Two calculation methods for significant dyads, that is, dyadic interactions with a clear dominant subordinate relationship in which one animal has won significantly more fights than its encounter, were proposed: pen individual limits were calculated by a sign test considering the differences of won and lost fights of all dyadic interactions in each pen; dyad individual limits were determined by a one-sided sign test for each individual dyad. For all data sets (ALL, including all dyadic interactions; PEN or DYAD, including only significant dyads according to pen or dyad individual limits), networks were built based on the information of initiator and receiver with the pigs as nodes and the edges between them illustrating attacks. General network parameters describing the whole network structure and centrality parameters describing the position of each animal in the network were calculated. Both pen and dyad individual limits revealed only a small percentage of significant dyads for weaned piglets (12.4% or 8.8%), fattening pigs (4.2% or 0.6%) and gilts (3.6% or 0.4%). The comparison between the data sets revealed only high Spearman’s rank correlation coefficients (rS) for the density, that is, percentage of possible edges that were actually present in the network, whereas the centrality parameters showed only moderate rS values (0.37 to 0.75). Thus, the rank order of the animals changed due to the exclusion of insignificant dyads, which shows that the results obtained from social network analysis are clearly influenced if insignificant dyads are excluded from the analyses. Due to the fact that the pen individual limits consider the overall level of agonistic interactions within each pen, this calculation method should be preferred over the dyad individual limits. Otherwise, too many animals in the group became isolated nodes with zero centrality for which no statement about their position within the network can be made.

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“…Furthermore, contacts between animals as well as information on the usage of resources like feed, water, or lying areas can be analysed from position data using the methods of network analysis. Network analysis [ 30 , 31 ] has become a valuable instrument in animal sciences as it provides meaningful parameters to describe and research social structures [ 6 , 32 , 33 ], animal behaviour [ 11 , 34 ] or interactions [ 35 , 36 , 37 , 38 ] and disease spreading [ 39 , 40 , 41 , 42 , 43 ]).…”

Section: Introductionmentioning

confidence: 99%

“HerdGPS-Preprocessor”—A Tool to Preprocess Herd Animal GPS Data; Applied to Evaluate Contact Structures in Loose-Housing Horses

Salau

Hildebrandt

Czycholl

et al. 2020

Animals

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Sensors delivering information on the position of farm animals have been widely used in precision livestock farming. Global Positioning System (GPS) sensors are already known from applications in military, private and commercial environments, and their application in animal science is increasing. However, as trade-offs between sensor cost, battery life and sensor weight have to be made, GPS based studies scheduling long data collection periods and including a high number of animals, have to deal with problems like high hardware costs and data disruption during recharging of sensors. Furthermore, human–animal interaction due to sensor changing at the end of battery life interferes with the animal behaviour under analysis. The present study thus proposes a setting to deal with these challenges and offers the software tool “HerdGPS-Preprocessor”, because collecting position data from multiple animals nonstop for several weeks produces a high amount of raw data which needs to be sorted, preprocessed and provided in a suitable format per animal and day. The software tool “HerdGPS-Preprocessor” additionally outputs contact lists to enable a straight analysis of animal contacts. The software tool was exemplarily deployed for one month of daily and continuous GPS data of 40 horses in a loose-housing boarding facility in northern Germany. Contact lists were used to generate separate networks for every hour, which are then analysed with regard to the network parameter density, diameter and clique structure. Differences depending on the day and the day time could be observed. More dense networks with more and larger cliques were determined in the hours prior to the opening of additional pasture.

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Social network analysis - centrality parameters and individual network positions of agonistic behavior in pigs over three different age levels

Cited by 31 publications

References 44 publications

A systematic survey of centrality measures for protein-protein interaction networks

A systematic survey of centrality measures for protein-protein interaction networks

Social network analysis in pigs: impacts of significant dyads on general network and centrality parameters

“HerdGPS-Preprocessor”—A Tool to Preprocess Herd Animal GPS Data; Applied to Evaluate Contact Structures in Loose-Housing Horses

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