Limits...
Joint modeling of multiple social networks to elucidate primate social dynamics: I. maximum entropy principle and network-based interactions.

Chan S, Fushing H, Beisner BA, McCowan B - PLoS ONE (2013)

Bottom Line: Here we develop a bottom-up, iterative modeling approach based upon the maximum entropy principle.Using a rhesus macaque group as a model system, we jointly modeled and analyzed four different social behavioral networks at two different time points (one stable and one unstable) from a rhesus macaque group housed at the California National Primate Research Center (CNPRC).We report and discuss the inter-behavioral dynamics uncovered by our joint modeling approach with respect to social stability.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of California Davis, Davis, California, USA.

ABSTRACT
In a complex behavioral system, such as an animal society, the dynamics of the system as a whole represent the synergistic interaction among multiple aspects of the society. We constructed multiple single-behavior social networks for the purpose of approximating from multiple aspects a single complex behavioral system of interest: rhesus macaque society. Instead of analyzing these networks individually, we describe a new method for jointly analyzing them in order to gain comprehensive understanding about the system dynamics as a whole. This method of jointly modeling multiple networks becomes valuable analytical tool for studying the complex nature of the interaction among multiple aspects of any system. Here we develop a bottom-up, iterative modeling approach based upon the maximum entropy principle. This principle is applied to a multi-dimensional link-based distributional framework, which is derived by jointly transforming the multiple directed behavioral social network data, for extracting patterns of synergistic inter-behavioral relationships. Using a rhesus macaque group as a model system, we jointly modeled and analyzed four different social behavioral networks at two different time points (one stable and one unstable) from a rhesus macaque group housed at the California National Primate Research Center (CNPRC). We report and discuss the inter-behavioral dynamics uncovered by our joint modeling approach with respect to social stability.

Show MeSH

Related in: MedlinePlus

Plots of the change in total chi-squared value after the cumulative application of the four constraint functions for all bivariate networks.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3585323&req=5

pone-0051903-g004: Plots of the change in total chi-squared value after the cumulative application of the four constraint functions for all bivariate networks.

Mentions: Below we check whether the set of four functions , derived from exploring the interacting relationship between grooming and aggression, retain universal effects through all other pairwise behaviors on 2009 data and across data of different year 2011. We report the series of tables of our joint modeling analyses (see Tables 2 and 3, as well as Tables S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12). Figure 4 shows the decreasing Chi-squared values as the number of constraining functions increase, also see Tables 4 and 5. This means that as we increase more functions to describe associations, the expected distribution more closely approximates the observed distribution. These plots also show that for the most part, the 2011 data fits better than the 2009 data, so there may be less complex associations in 2011 just before the social overthrow.


Joint modeling of multiple social networks to elucidate primate social dynamics: I. maximum entropy principle and network-based interactions.

Chan S, Fushing H, Beisner BA, McCowan B - PLoS ONE (2013)

Plots of the change in total chi-squared value after the cumulative application of the four constraint functions for all bivariate networks.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585323&req=5

pone-0051903-g004: Plots of the change in total chi-squared value after the cumulative application of the four constraint functions for all bivariate networks.
Mentions: Below we check whether the set of four functions , derived from exploring the interacting relationship between grooming and aggression, retain universal effects through all other pairwise behaviors on 2009 data and across data of different year 2011. We report the series of tables of our joint modeling analyses (see Tables 2 and 3, as well as Tables S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12). Figure 4 shows the decreasing Chi-squared values as the number of constraining functions increase, also see Tables 4 and 5. This means that as we increase more functions to describe associations, the expected distribution more closely approximates the observed distribution. These plots also show that for the most part, the 2011 data fits better than the 2009 data, so there may be less complex associations in 2011 just before the social overthrow.

Bottom Line: Here we develop a bottom-up, iterative modeling approach based upon the maximum entropy principle.Using a rhesus macaque group as a model system, we jointly modeled and analyzed four different social behavioral networks at two different time points (one stable and one unstable) from a rhesus macaque group housed at the California National Primate Research Center (CNPRC).We report and discuss the inter-behavioral dynamics uncovered by our joint modeling approach with respect to social stability.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of California Davis, Davis, California, USA.

ABSTRACT
In a complex behavioral system, such as an animal society, the dynamics of the system as a whole represent the synergistic interaction among multiple aspects of the society. We constructed multiple single-behavior social networks for the purpose of approximating from multiple aspects a single complex behavioral system of interest: rhesus macaque society. Instead of analyzing these networks individually, we describe a new method for jointly analyzing them in order to gain comprehensive understanding about the system dynamics as a whole. This method of jointly modeling multiple networks becomes valuable analytical tool for studying the complex nature of the interaction among multiple aspects of any system. Here we develop a bottom-up, iterative modeling approach based upon the maximum entropy principle. This principle is applied to a multi-dimensional link-based distributional framework, which is derived by jointly transforming the multiple directed behavioral social network data, for extracting patterns of synergistic inter-behavioral relationships. Using a rhesus macaque group as a model system, we jointly modeled and analyzed four different social behavioral networks at two different time points (one stable and one unstable) from a rhesus macaque group housed at the California National Primate Research Center (CNPRC). We report and discuss the inter-behavioral dynamics uncovered by our joint modeling approach with respect to social stability.

Show MeSH
Related in: MedlinePlus