The social network structure of a wild meerkat population: 2. Intragroup interactions

2009 ◽  
Vol 64 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Joah R. Madden ◽  
Julian A. Drewe ◽  
Gareth P. Pearce ◽  
Tim H. Clutton-Brock
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Social Network Structure ◽  
The Social

scholarly journals Formation of raiding parties for intergroup violence is mediated by social network structure

2016 ◽  
Vol 113 (43) ◽  
pp. 12114-12119 ◽  
Author(s):  
Luke Glowacki ◽  
Alexander Isakov ◽  
Richard W. Wrangham ◽  
Rose McDermott ◽  
James H. Fowler ◽  
...  
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Social Dynamics ◽  
Small Scale ◽  
East African ◽  
Nomadic Pastoralists ◽  
Social Network Structure ◽  
The Social ◽  
Total Impact ◽  
Social Network Position

Intergroup violence is common among humans worldwide. To assess how within-group social dynamics contribute to risky, between-group conflict, we conducted a 3-y longitudinal study of the formation of raiding parties among the Nyangatom, a group of East African nomadic pastoralists currently engaged in small-scale warfare. We also mapped the social network structure of potential male raiders. Here, we show that the initiation of raids depends on the presence of specific leaders who tend to participate in many raids, to have more friends, and to occupy more central positions in the network. However, despite the different structural position of raid leaders, raid participants are recruited from the whole population, not just from the direct friends of leaders. An individual’s decision to participate in a raid is strongly associated with the individual’s social network position in relation to other participants. Moreover, nonleaders have a larger total impact on raid participation than leaders, despite leaders’ greater connectivity. Thus, we find that leaders matter more for raid initiation than participant mobilization. Social networks may play a role in supporting risky collective action, amplify the emergence of raiding parties, and hence facilitate intergroup violence in small-scale societies.

The social network structure of a semi-free roaming European bison herd (Bison bonasus)

2019 ◽  
Vol 158 ◽  
pp. 97-105 ◽  
Author(s):  
Amandine Ramos ◽  
Lola Manizan ◽  
Esther Rodriguez ◽  
Yvonne J.M. Kemp ◽  
Cédric Sueur
Keyword(s):  
Social Network ◽  
Network Structure ◽  
European Bison ◽  
Bison Bonasus ◽  
Social Network Structure ◽  
The Social

scholarly journals Offspring social network structure predicts fitness in families

2012 ◽  
Vol 279 (1749) ◽  
pp. 4914-4922 ◽  
Author(s):  
Nick J. Royle ◽  
Thomas W. Pike ◽  
Philipp Heeb ◽  
Heinz Richner ◽  
Mathias Kölliker
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Conflicts Of Interest ◽  
Parus Major ◽  
Social Structures ◽  
Emergent Properties ◽  
Social Network Structure ◽  
The Social ◽  
The Individual

Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

Estimating Policy Positions Using Social Network Data

2016 ◽  
Vol 35 (1) ◽  
pp. 53-67 ◽  
Author(s):  
Alejandro Ecker
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Science Research ◽  
Policy Position ◽  
Policy Positions ◽  
Political Actors ◽  
External Data ◽  
Social Network Structure ◽  
Big Data Applications ◽  
The Social

Social network site (SNS) data provide scholars with a plethora of new opportunities for studying public opinion and forecasting electoral outcomes. While these are certainly among the most promising big data applications in political science research, a series of pioneering studies have started to uncover the vast potential of such data to estimate the policy positions of political actors. Adding to this emerging strand in the scholarly literature, the present article explores the validity of (individual) policy positions derived from the social network structure of the microblogging platform Twitter. At the aggregate party level, cross-validation with external data sources suggests that SNS data provide valid policy position estimates. In contrast, the empirical analysis reveals only a moderate connection between individual policy positions retrieved from the social network structure and those retrieved from members of parliament individual voting record. These results thus highlight the potential as well as important limitations of SNS data in indicating the policy positions of political parties and individual legislators.

scholarly journals Classification on the social network structure in the local community organization by application of social embedded concept

2011 ◽  
Vol 30 (Special_Issue) ◽  
pp. 357-362
Author(s):  
Kazu HAGIHARA ◽  
Satoshi HOSHINO ◽  
Shizuka HASHIMOTO ◽  
Yasuaki KUKI
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Local Community ◽  
Community Organization ◽  
Social Network Structure ◽  
The Social

scholarly journals Modelling the dispersion of SARS-CoV-2 on a dynamic network graph

2020 ◽  
Author(s):  
Patrick Bryant ◽  
Arne Elofsson
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Preferential Attachment ◽  
Age Groups ◽  
Dynamic Network ◽  
Social Contacts ◽  
Social Network Structure ◽  
The Social ◽  
Epidemic Development ◽  
The Impact

AbstractBackgroundWhen modelling the dispersion of an epidemic using R0, one only considers the average number of individuals each infected individual will infect. However, we know from extensive studies of social networks that there is significant variation in the number of connections and thus social contacts each individual has. Individuals with more social contacts are more likely to attract and spread infection. These individuals are likely the drivers of the epidemic, so-called superspreaders. When many superspreaders are immune, it becomes more difficult for the disease to spread, as the connectedness of the social network dramatically decreases. If one assumes all individuals being equally connected and thus as likely to spread disease as in a SIR model, this is not true.MethodsTo account for the impact of social network structure on epidemic development, we model the dispersion of SARS-CoV-2 on a dynamic preferential attachment graph which changes appearance proportional to observed mobility changes. We sample a serial interval distribution that determines the probability of dispersion for all infected nodes each day. We model the dispersion in different age groups using age-specific infection fatality rates. We vary the infection probabilities in different age groups and analyse the outcome.ResultsThe impact of movement on network dynamics plays a crucial role in the spread of infections. We find that higher movement results in higher spread due to an increased probability of new connections being made within a social network. We show that saturation in the dispersion can be reached much earlier on a preferential attachment graph compared to spread on a random graph, which is more similar to estimations using R0.ConclusionsWe provide a novel method for modelling epidemics by using a dynamic network structure related to observed mobility changes. The social network structure plays a crucial role in epidemic development, something that is often overlooked.

Information Flow within the Social Network Structure of a Joint Strategic Plan for Management of Great Lakes Fisheries

2011 ◽  
Vol 31 (4) ◽  
pp. 629-655 ◽  
Author(s):  
Nancy J. Leonard ◽  
William W. Taylor ◽  
Chris I. Goddard ◽  
Kenneth A. Frank ◽  
Ann E. Krause ◽  
...  
Keyword(s):  
Social Network ◽  
Great Lakes ◽  
Information Flow ◽  
Network Structure ◽  
Strategic Plan ◽  
Social Network Structure ◽  
The Social
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