scholarly journals Social Network Analysis of a Chimpanzee (Pan troglodytes) Group in Captivity Following the Integration of a New Adult Member

2020 ◽  
Vol 41 (5) ◽  
pp. 683-700
Author(s):  
Sergio Díaz ◽  
Lindsay Murray ◽  
Sam G. B. Roberts ◽  
Paul Rodway
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Pan Troglodytes ◽  
Group Structure ◽  
First Year ◽  
Primate Behavior ◽  
Significant Difference ◽  
In Captivity ◽  
The Stability

AbstractManagement of primates in captivity often presents the challenge of introducing new individuals into a group, and research investigating the stability of the social network in the medium term after the introduction can help inform management decisions. We investigated the behavior of a group of chimpanzees (Pan troglodytes) housed at Chester Zoo, UK over 12 months (divided into three periods of 4 months) following the introduction of a new adult female. We recorded grooming, proximity, other affiliative behaviors, and agonistic behaviors and used social network analysis to investigate the stability, reciprocity, and structure of the group, to examine the effect of rearing history on grooming network position and the role of sex in agonistic behavior. Both the grooming and agonistic networks correlated across all three periods, while affiliative networks correlated only between periods 2 and 3. Males had significantly higher out-degree centrality in agonistic behaviors than females, indicating that they carried out agonistic behaviors more often than females. There was no significant difference in centrality between hand-reared and mother-reared chimpanzees. Overall, the group structure was stable and cohesive during the first year after the introduction of the new female, suggesting that this change did not destabilize the group. Our findings highlight the utility of social network analysis in the study of primate sociality in captivity, and how it can be used to better understand primate behavior following the integration of new individuals.

Collaboration Networks of the Implementation Science Centers for Cancer Control (ISC3): A Social Network Analysis

2021 ◽  
Author(s):  
Rebekah R Jacob ◽  
Ariella R Korn ◽  
Grace C Huang ◽  
Douglas Easterling ◽  
Daniel A Gundersen ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Implementation Science ◽  
Scientific Collaboration ◽  
Cancer Control ◽  
First Year ◽  
Science Centers ◽  
Policy Dissemination ◽  
Conducting Research

Abstract Background: Multi-center research initiatives offer opportunities to develop and strengthen connections among researchers. These initiatives often have goals of increased scientific collaboration which can be examined using social network analysis.Methods: The National Cancer Institute (NCI)-funded Implementation Science Centers in Cancer Control (ISC3) initiative conducted an online social network survey in its first year of funding (2020) to examine early scientific linkages among members (faculty, staff, trainees) and recognize areas for network growth. Members of the seven funded centers and NCI program staff identified collaborations in: planning/conducting research, capacity building, product development, scientific dissemination, practice/policy dissemination.Results: Of the 192 invitees, 182 network members completed the survey (95%). The most prevalent roles were faculty (60%) and research staff (24%). Almost one-quarter (23%) of members reported advanced expertise in implementation science (IS), 42% intermediate, and 35% beginner. Most members were female (69%) and white (79%). Across all collaboration activities, the network had a density of 14%, suggesting high cohesion for its first year. One-third (33%) of collaboration ties were between members from different centers. Degree centralization (0.33) and betweenness centralization (0.07) measures suggest a fairly saturated network (no one or few central member(s) holding all connections). The most prevalent and densely connected collaboration network was for planning/conducting research (1470 ties; 8% density). Practice/policy dissemination had the fewest collaboration ties (284), lowest density (3%), and largest number of non-connected members (n=43). Median degree (number of collaborations) varied across member characteristics and collaboration activities. Members with advanced IS expertise were more connected than intermediate/beginner groups for most activities (e.g., advanced IS members had a median of 24 capacity building collaborations (range: 4-58) vs. intermediate (median 9; range 2-53) and beginner (median 7; range 1-49) members. The number of practice/policy dissemination collaborations were similarly low across IS expertise levels (median degree 3 for advanced, 2 intermediate, 2 beginner). Conclusions: Results provide important directions for interventions within the ISC3 network to increase scientific collaboration and capacity, with a focus on growing cross-center collaborations and increasing engagement of under-represented groups. Findings will be used to capture infrastructure development as part of the initiative’s evaluation.

Network Analysis for Economics and Management Studies

2016 ◽  
pp. 1-60 ◽  
Author(s):  
Lucio Biggiero
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Formation ◽  
Competitive Equilibrium ◽  
Group Structure ◽  
Network Modeling ◽  
Descriptive Analysis ◽  
Economic Networks ◽  
Main Research

Sociology and other social sciences have employed network analysis earlier than management and organization sciences, and much earlier than economics, which has been the last one to systematically adopt it. Nevertheless, the development of network economics during last 15 years has been massive, alongside three main research streams: strategic formation network modeling, (mostly descriptive) analysis of real economic networks, and optimization methods of economic networks. The main reason why this enthusiastic and rapidly diffused interest of economists came so late is that the most essential network properties, like externalities, endogenous change processes, and nonlinear propagation processes, definitely prevent the possibility to build a general – and indeed even partial – competitive equilibrium theory. For this paradigm has dominated economics in the last century, this incompatibility operated as a hard brake, and presented network analysis as an inappropriate epistemology. Further, being intrinsically (and often, until recent times, also radically) structuralist, social network analysis was also antithetic to radical methodological individualism, which was – and still is – economics dominant methodology. Though culturally and scientifically influenced by economists in some fields, like finance, banking and industry studies, scholars in management and organization sciences were free from “neoclassical economics chains”, and therefore more ready and open to adopt the methodology and epistemology of social network analysis. The main and early field through which its methods were channeled was the sociology of organizations, and in particular group structure and communication, because this is a research area largely overlapped between sociology and management studies. Currently, network analysis is becoming more and more diffused within management and organization sciences. Mostly descriptive until 15 years ago, all the fields of social network analysis have a great opportunity of enriching and developing its methods of investigation through statistical network modeling, which offers the possibility to develop, respectively, network formation and network dynamics models. They are a good compromise between the much more powerful agent-based simulation models and the usually descriptive (or poorly analytical) methods.

scholarly journals Agonistic Interactions in Pigs–Comparison of Dominance Indices with Parameters Derived from Social Network Analysis in Three Age Groups

Animals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 929 ◽  
Author(s):  
Kathrin Büttner ◽  
Irena Czycholl ◽  
Katharina Mees ◽  
Joachim Krieter
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Structural Equation ◽  
Dominance Hierarchy ◽  
Rank Order ◽  
Group Structure ◽  
Age Groups ◽  
Agonistic Interactions ◽  
Dominance Indices

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.

How can social network analysis improve the study of primate behavior?

2010 ◽  
Vol 73 (8) ◽  
pp. 703-719 ◽  
Author(s):  
Cédric Sueur ◽  
Armand Jacobs ◽  
Frédéric Amblard ◽  
Odile Petit ◽  
Andrew J. King
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Primate Behavior

Influence of Opinion Leaders on Purchase Intention Using Social Network Analysis

2018 ◽  
Vol 10 (1) ◽  
pp. 1-16
Author(s):  
Yi-Fen Chen ◽  
Chia-Wen Tsai ◽  
Yu-Fu Ann
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Purchase Intention ◽  
Mobile Apps ◽  
Peer Group ◽  
Group Structure ◽  
Perceived Value ◽  
Opinion Leader ◽  
Network Centrality

This article examines social network centralities to identify peer group's opinion leader with the aim of determining whether an opinion leader and perceived value influence purchase intention in the field of paid mobile apps. Social network analysis (SNA) and regression analysis are applied to examine the hypotheses within the theoretical framework. The experiment involved a peer group of college students with total of 46 subjects. Using SPSS to analyze the influences of perceived value and the group's opinion leader on purchase intention, the results showed that consumer purchase intention is positively influenced by both the perceived value of paid mobile apps and positive advices given by opinion leader. In addition, an analysis using Ucinet 6 to examine consulting network centrality, friendship network centrality, and information centrality of every member of the group revealed that based on group structure, the group member having the highest centrality has the group's potential to be the opinion leader.

scholarly journals Dissent and rebellion in the House of Commons: a social network analysis of Brexit-related divisions in the 57th Parliament

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Carla Intal ◽  
Taha Yasseri
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
European Integration ◽  
Party System ◽  
House Of Commons ◽  
Pairwise Similarity ◽  
Significant Difference ◽  
The Individual ◽  
Similarity Scores

AbstractThe British party system is known for its discipline and cohesion, but it remains wedged on one issue: European integration. We offer a methodology using social network analysis that considers the individual interactions of MPs in the voting process. Using public Parliamentary records, we scraped votes of individual MPs in the 57th parliament (June 2017 to April 2019), computed pairwise similarity scores and calculated rebellion metrics based on eigenvector centralities. Comparing the networks of Brexit- and non-Brexit divisions, our methodology was able to detect a significant difference in eurosceptic behaviour for the former, and using a rebellion metric we predicted how MPs would vote in a forthcoming Brexit deal with over 90% accuracy.

Network Analysis for Economics and Management Studies

2020 ◽  
pp. 269-328
Author(s):  
Lucio Biggiero
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Formation ◽  
Competitive Equilibrium ◽  
Group Structure ◽  
Network Modeling ◽  
Descriptive Analysis ◽  
Economic Networks ◽  
Main Research

Sociology and other social sciences have employed network analysis earlier than management and organization sciences, and much earlier than economics, which has been the last one to systematically adopt it. Nevertheless, the development of network economics during last 15 years has been massive, alongside three main research streams: strategic formation network modeling, (mostly descriptive) analysis of real economic networks, and optimization methods of economic networks. The main reason why this enthusiastic and rapidly diffused interest of economists came so late is that the most essential network properties, like externalities, endogenous change processes, and nonlinear propagation processes, definitely prevent the possibility to build a general – and indeed even partial – competitive equilibrium theory. For this paradigm has dominated economics in the last century, this incompatibility operated as a hard brake, and presented network analysis as an inappropriate epistemology. Further, being intrinsically (and often, until recent times, also radically) structuralist, social network analysis was also antithetic to radical methodological individualism, which was – and still is – economics dominant methodology. Though culturally and scientifically influenced by economists in some fields, like finance, banking and industry studies, scholars in management and organization sciences were free from “neoclassical economics chains”, and therefore more ready and open to adopt the methodology and epistemology of social network analysis. The main and early field through which its methods were channeled was the sociology of organizations, and in particular group structure and communication, because this is a research area largely overlapped between sociology and management studies. Currently, network analysis is becoming more and more diffused within management and organization sciences. Mostly descriptive until 15 years ago, all the fields of social network analysis have a great opportunity of enriching and developing its methods of investigation through statistical network modeling, which offers the possibility to develop, respectively, network formation and network dynamics models. They are a good compromise between the much more powerful agent-based simulation models and the usually descriptive (or poorly analytical) methods.

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