scholarly journals Knockouts of high-ranking males have limited impact on baboon social networks

2015 ◽  
Vol 61 (1) ◽  
pp. 107-113 ◽  
Author(s):  
Mathias Franz ◽  
Jeanne Altmann ◽  
Susan C. Alberts
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Adult Female ◽  
Structural Changes ◽  
Clustering Coefficient ◽  
Network Structures ◽  
High Ranking ◽  
Transmission Of Information ◽  
Term Data

Abstract Social network structures can crucially impact complex social processes such as collective behaviour or the transmission of information and diseases. However, currently it is poorly understood how social networks change over time. Previous studies on primates suggest that ‘knockouts’ (due to death or dispersal) of high-ranking individuals might be important drivers for structural changes in animal social networks. Here we test this hypothesis using long-term data on a natural population of baboons, examining the effects of 29 natural knockouts of alpha or beta males on adult female social networks. We investigated whether and how knockouts affected (1) changes in grooming and association rates among adult females, and (2) changes in mean degree and global clustering coefficient in these networks. The only significant effect that we found was a decrease in mean degree in grooming networks in the first month after knockouts, but this decrease was rather small, and grooming networks rebounded to baseline levels by the second month after knockouts. Taken together our results indicate that the removal of high-ranking males has only limited or no lasting effects on social networks of adult female baboons. This finding calls into question the hypothesis that the removal of high-ranking individuals has a destabilizing effect on social network structures in social animals.

Comparing the social networks of service users with long term mental health needs living in community with those in a general adult in-patient unit

2021 ◽  
pp. 002076402110175
Author(s):  
Roberto Rusca ◽  
Ike-Foster Onwuchekwa ◽  
Catherine Kinane ◽  
Douglas MacInnes
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Patient Group ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Practical Support ◽  
The Social

Background: Relationships are vital to recovery however, there is uncertainty whether users have different types of social networks in different mental health settings and how these networks may impact on users’ wellbeing. Aims: To compare the social networks of people with long-term mental illness in the community with those of people in a general adult in-patient unit. Method: A sample of general adult in-patients with enduring mental health problems, aged between 18 and 65, was compared with a similar sample attending a general adult psychiatric clinic. A cross-sectional survey collected demographic data and information about participants’ social networks. Participants also completed the Short Warwick Edinburgh Mental Well-Being Scale to examine well-being and the Significant Others Scale to explore their social network support. Results: The study recruited 53 participants (25 living in the community and 28 current in-patients) with 339 named as important members of their social networks. Both groups recorded low numbers in their social networks though the community sample had a significantly greater number of social contacts (7.4 vs. 5.4), more monthly contacts with members of their network and significantly higher levels of social media use. The in-patient group reported greater levels of emotional and practical support from their network. Conclusions: People with serious and enduring mental health problems living in the community had a significantly greater number of people in their social network than those who were in-patients while the in-patient group reported greater levels of emotional and practical support from their network. Recommendations for future work have been made.

Simulating Social Network Formation

2011 ◽  
pp. 581-599
Author(s):  
Robert Gilles ◽  
Tabitha James ◽  
Reza Barkhi ◽  
Dimitrios Diamantaras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Network Formation ◽  
Theoretic Model ◽  
Network Structures ◽  
Agent Behavior ◽  
Graph Theoretic ◽  
Model And Simulation ◽  
Graph Theoretic Models

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

Motif Analysis and the Periodic Structural Changes in an Organizational Email-Based Social Network

2011 ◽  
pp. 292-302
Author(s):  
Krzysztof Juszczyszyn ◽  
Katarzyna Musial
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Topology ◽  
Structural Changes ◽  
Structural Parameters ◽  
Local Network ◽  
Network Motifs ◽  
Motif Analysis ◽  
University Of Technology ◽  
E Mail

Network motifs are small subgraphs that reflect local network topology and were shown to be useful for creating profiles that reveal several properties of the network. In this work the motif analysis of the e-mail network of the Wroclaw University of Technology, consisting of over 4000 nodes was conducted. Temporal changes in the network structure during the period of 20 months were analysed and the correlations between global structural parameters of the network and motif distribution were found. These results are to be used in the development of methods dedicated for fast estimating of the properties of complex internet-based social networks.

Maintenance of cultural diversity: Social roles, social networks, and cognitive networks

2014 ◽  
Vol 37 (3) ◽  
pp. 254-255 ◽  
Author(s):  
Marshall Abrams
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Cultural Diversity ◽  
Social Roles ◽  
Cognitive Networks ◽  
Network Structures ◽  
Group Level ◽  
Individual Level ◽  
Cultural Traits ◽  
Maintenance Of Diversity

AbstractSmaldino suggests that patterns that give rise to group-level cultural traits can also increase individual-level cultural diversity. I distinguish social roles and related social network structures and discuss ways in which each might maintain diversity. I suggest that cognitive analogs of “cohesion,” a property of networks that helps maintenance of diversity, might mediate the effects of social roles on diversity.

A novel genetic algorithm-based improvement model for online communities and trust networks

2021 ◽  
Vol 40 (1) ◽  
pp. 1597-1608
Author(s):  
Ilker Bekmezci ◽  
Murat Ermis ◽  
Egemen Berki Cimen
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Online Communities ◽  
Real Life ◽  
Statistical Properties ◽  
Clustering Coefficient ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.

Building Complex Network Similar to Facebook

2014 ◽  
Vol 513-517 ◽  
pp. 909-913
Author(s):  
Dong Wei Guo ◽  
Xiang Yan Meng ◽  
Cai Fang Hou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Clustering Coefficient ◽  
Network Characteristics ◽  
Modeling Methods ◽  
Community Strength ◽  
Strength Based

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

scholarly journals Modelling the Diffusion of Investment Decisions on Modular Social Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiaokang Cheng ◽  
Narisa Zhao
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Diffusion Model ◽  
Financial Market ◽  
Negative Impact ◽  
Investment Decisions ◽  
Negative Influence ◽  
Clustering Coefficient ◽  
The Impact

In the financial market, information and investment behaviors disseminate in investor social networks, and different contagion patterns may cause diverse investment trends. Prior studies have investigated the impact of investor social networks, but few have considered community structure. In this paper, we study the impact of the community structure of investor social networks on the diffusion of internet investment products. A two-stage diffusion model is proposed, and the clustering coefficient and modularity of an investor social network are considered. The results show that both modularity and the clustering coefficient have an impact on the diffusion velocity and scale and that the impact is most evident at the stage of explosive growth. The negative influence of a large modularity can be hardly mitigated by adjusting other factors. Furthermore, a decrease in modularity and an increase in the clustering coefficient can better facilitate diffusion when the temporary investment rate is high and can partly offset the negative impact of information discarding and divestment.

Simulating Social Network Formation

2009 ◽  
Vol 1 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Robert Gilles ◽  
Tabitha James ◽  
Reza Barkhi ◽  
Dimitrios Diamantaras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Network Formation ◽  
Theoretic Model ◽  
Network Structures ◽  
Agent Behavior ◽  
Graph Theoretic ◽  
Systems Research ◽  
Model And Simulation

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

Networks of Co-Authorship

2012 ◽  
pp. 981-994
Author(s):  
Antonio José Caulliraux Pithon ◽  
Ralfh Varges Ansuattigui ◽  
Paulo Enrique Stecklow
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Morphological Characteristics ◽  
Network Structures ◽  
Communication Processes ◽  
The Social ◽  
The Subject

The networks are transorganizational arrangements forming a structure and, in a more abstract and generic manner, are built from the interactions between individuals and organizations. These interactions allow the emergence of network structures more related to personal ties and the types of existing social interactions between the actors. Social networks aren’t a recent enterprise, but have been the subject of deeper studies due to universalization and convergence of communication processes, fundamental to the establishment and proliferation of networks. The structure where networks are manifested calls for horizontality, where there is no formal hierarchy of the elements that comprise it, composed by nodes elements and lines elements. This article analyzes the social network of authorship of one of five Postgraduate Programs of CEFET/RJ, presenting the connections between network teachers, justifying the morphological characteristics of the network and suggesting methodologies for continuing the study for the teaching and researching networks.

Codiffusion of Technologies in Social Networks

2020 ◽  
Vol 12 (4) ◽  
pp. 193-228
Author(s):  
Natalia Lazzati
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Diffusion Process ◽  
Technology Diffusion ◽  
Consideration Sets ◽  
Network Structures ◽  
The Social ◽  
Over Time

This paper studies the diffusion process of two complementary technologies among people who are connected through a social network. It characterizes adoption rates over time for different initial allocations and network structures. In doing so, we provide some microfoundations for the stochastic formation of consideration sets. We are particularly interested in the following question: suppose we want to maximize technology diffusion and have a limited number of units of each of the two technologies to initially distribute—how should we allocate these units among people in the social network? (JEL D83, O33, Z13)

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