scholarly journals Toward Curation and Personality-Driven Social Networks

2020 ◽  
Author(s):  
Joseph Bayer ◽  
Bas Hofstra
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Social Stratification ◽  
Network Formation ◽  
Research Question ◽  
Network Size ◽  
Personal Network ◽  
Online Technologies ◽  
Emergent Technologies ◽  
Stable Network

Do humans have bigger or smaller social networks today? We reflect on the state of this research question and assert that an updated approach is needed to understand the effects of emergent technologies on network structure. Although the absolute changes in average network size are likely to remain elusive, recent perspectives converge on the idea that online technologies make it easier for individuals to shape—or curate—their social connections. Here we merge conflicting views and specify mechanisms through which curation technologies may impact personal network structure. Looking forward, we suggest personality will become more influential in network formation and maintenance when aided by technological levers. Consequently, curation technologies have the potential to increase differences in networks between types of people (for example, extroverts vs introverts) and thus generate new forms of social stratification, despite preserving a stable network size on average. The Comment concludes with empirical and theoretical implications, including the importance of attending to dispersion and examining the societal ramifications of personality-driven curation.

scholarly journals Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

2020 ◽  
Vol 11 (1) ◽  
pp. 18-24
Author(s):  
Morgan Prust ◽  
Abby Halm ◽  
Simona Nedelcu ◽  
Amber Nieves ◽  
Amar Dhand
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Isolation ◽  
Network Structure ◽  
Network Size ◽  
Personal Network ◽  
Mapping Tool ◽  
Social Network Structure ◽  
Network Mapping

Background and Purpose: Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale–Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member. Methods: In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET’s network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size. Results: We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5. Conclusions: Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

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.

scholarly journals Social Network Structure Is Related to Functional Improvement From Home-Based Telerehabilitation After Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Archana Podury ◽  
Sophia M. Raefsky ◽  
Lucy Dodakian ◽  
Liam McCafferty ◽  
Vu Le ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Depressive Symptoms ◽  
Network Structure ◽  
Network Size ◽  
Network Density ◽  
Stroke Patients ◽  
Historical Cohort ◽  
Home Based ◽  
Social Network Structure

Objective: Telerehabilitation (TR) is now, in the context of COVID-19, more clinically relevant than ever as a major source of outpatient care. The social network of a patient is a critical yet understudied factor in the success of TR that may influence both engagement in therapy programs and post-stroke outcomes. We designed a 12-week home-based TR program for stroke patients and evaluated which social factors might be related to motor gains and reduced depressive symptoms.Methods: Stroke patients (n = 13) with arm motor deficits underwent supervised home-based TR for 12 weeks with routine assessments of motor function and mood. At the 6-week midpoint, we mapped each patient's personal social network and evaluated relationships between social network metrics and functional improvements from TR. Finally, we compared social networks of TR patients with a historical cohort of 176 stroke patients who did not receive any TR to identify social network differences.Results: Both network size and network density were related to walk time improvement (p = 0.025; p = 0.003). Social network density was related to arm motor gains (p = 0.003). Social network size was related to reduced depressive symptoms (p = 0.015). TR patient networks were larger (p = 0.012) and less dense (p = 0.046) than historical stroke control networks.Conclusions: Social network structure is positively related to improvement in motor status and mood from TR. TR patients had larger and more open social networks than stroke patients who did not receive TR. Understanding how social networks intersect with TR outcomes is crucial to maximize effects of virtual rehabilitation.

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.

The Stability and Efficiency of Network Structure with Joining Costs

2014 ◽  
Vol 526 ◽  
pp. 357-361
Author(s):  
Fan Zhang
Keyword(s):  
Network Structure ◽  
Network Formation ◽  
Evolutionary Process ◽  
Pairwise Stability ◽  
Link Model ◽  
The Stability ◽  
Networks Structure ◽  
Stable Network

This paper aim to study the stability and efficiency of a social and economic network in which the various elements are closely related by the network structure, while self-interested individuals can form or sever links with the network by incurring joining costs. Our novel feature is that we consider joining costs in network formation. Firstly, We also propose the concept of a stable network that is similar to the pairwise stability of Jackson and Wolinsky (1996) based on joining costs. We examine changes in the stability, efficiency and network structure. For a link model, we identify the main characteristics of stable and efficient networks. It is important to note that a stable network is not always efficient. Next, we show that the range of stable networks has been extended through the evolutionary process of developing networks with joining costs. Moreover, consistency, stability and efficiency also enhance the networks structure.

Fast detection of the fuzzy communities based on leader-driven algorithm

2018 ◽  
Vol 32 (06) ◽  
pp. 1850058
Author(s):  
Changjian Fang ◽  
Dejun Mu ◽  
Zhenghong Deng ◽  
Jun Hu ◽  
Chen-He Yi
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Learning Community ◽  
Network Structure ◽  
Fast Algorithm ◽  
Network Size ◽  
Local Minima ◽  
Large Networks ◽  
Number Of Clusters ◽  
Fast Detection

In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

scholarly journals A meritocratic network formation model for the rise of social media influencers

2021 ◽  
Author(s):  
Nicolò Pagan ◽  
Wenjun Mei ◽  
Cheng Li ◽  
Florian Dörfler
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Network Formation ◽  
Large Data ◽  
Network Size ◽  
Large Data Sets ◽  
Data Sets ◽  
Strategic Incentives ◽  
Quality Ranking ◽  
Social Network Formation

Abstract Many of today’s most used online social networks such as Instagram, Youtube, Twitter, or Twitch are based on User-Generated Content (UGC), and the exploration of this content is enhanced by the integrated search engines. Prior multidisciplinary effort on studying social network formation processes has privileged topological elements or socio-strategic incentives. Here, we propose an untouched meritocratic approach inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We statistically and numerically analyze the network equilibria properties: while the expected outdegree of the nodes remains bounded by the logarithm of the network size, the expected indegree follows a Zipf’s law with respect to the quality ranking. Notably, our quality-based mechanism provides an intuitive explanation of the origin of the Zipf’s regularity in growing social networks. Our theoretical results are empirically validated against large data-sets collected from Twitch, a fast-growing platform for online gamers.

scholarly journals Social network structure and composition in former NFL football players

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amar Dhand ◽  
Liam McCafferty ◽  
Rachel Grashow ◽  
Ian M. Corbin ◽  
Sarah Cohan ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
National Football League ◽  
Treatment Options ◽  
Network Size ◽  
Brain Trauma ◽  
Future Research ◽  
Football Players ◽  
Contact Sports

AbstractSocial networks have broad effects on health and quality of life. Biopsychosocial factors may also modify the effects of brain trauma on clinical and pathological outcomes. However, social network characterization is missing in studies of contact sports athletes. Here, we characterized the personal social networks of former National Football League players compared to non-football US males. In 303 former football players and 269 US males, we found that network structure (e.g., network size) did not differ, but network composition (e.g., proportion of family versus friends) did differ. Football players had more men than women, and more friends than family in their networks compared to US males. Black players had more racially diverse networks than White players and US males. These results are unexpected because brain trauma and chronic illnesses typically cause diminished social relationships. We anticipate our study will inform more multi-dimensional study of, and treatment options for, contact sports athletes. For example, the strong allegiances of former athletes may be harnessed in the form of social network interventions after brain trauma. Because preserving health of contact sports athletes is a major goal, the study of social networks is critical to the design of future research and treatment trials.

Important relationships in a multilevel world: The role of network structure in explaining closeness of relationships and access to resources in later life

2021 ◽  
pp. 026540752110093
Author(s):  
Mahin Raissi ◽  
Robert Ackland
Keyword(s):  
Social Networks ◽  
Statistical Model ◽  
Network Structure ◽  
Online Social Networks ◽  
Later Life ◽  
Personal Networks ◽  
Network Data ◽  
Personal Network ◽  
Access To Resources

We examine why some relationships are more important than others, using a multilevel statistical model and data on personal networks of Australians 50 years and older, collected via a purpose-built Facebook application. While the network data were collected automatically, participants in our study provided data on the importance of their relationships, measured by perceived closeness and access to resources. We find that the information on how network members are connected with each other (network structure) provides powerful insights into what makes a relationship important. When importance is measured by closeness of relationship, important alters are kin, and are alters who are highly connected with others or act as bridges between different groups. When importance is measured by access to resources, important alters are those who act as bridges and are in more densely-knit networks. We discuss our findings in a broader context of research into important relationships in later life, and collecting personal network data via online social networks.

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