Performance of procedures for identifying influentials in a social network: prediction of time and memory usage as a function of network properties

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
P. M. Krishnaraj ◽  
Ankith Mohan ◽  
K. G. Srinivasa
Keyword(s):  
Social Network ◽  
Memory Usage ◽  
Network Properties ◽  
Network Prediction

scholarly journals Dynamic network prediction

2020 ◽  
Vol 8 (4) ◽  
pp. 574-595
Author(s):  
Ravi Goyal ◽  
Victor De Gruttola
Keyword(s):  
Social Relationships ◽  
Network Structure ◽  
Broad Class ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Simulation Studies ◽  
Population Composition ◽  
Statistical Framework ◽  
Network Properties ◽  
Network Prediction

AbstractWe present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the U.S. Senate from 2003 to 2016.

Athletes as Advocates: Influencing Eating-Disorder Beliefs and Perceptions Through Social Media

2018 ◽  
Vol 11 (4) ◽  
pp. 433-446 ◽  
Author(s):  
Fallon R. Mitchell ◽  
Sara Santarossa ◽  
Sarah J. Woodruff
Keyword(s):  
Social Media ◽  
Eating Disorder ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Text Analysis ◽  
Social Issues ◽  
Penn State ◽  
Network Properties

The present study aimed to explore the interactions and influences that occurred on Twitter after Joey Julius’s (NCAA athlete, Penn State Football) and Mike Marjama’s (MLB player, Seattle Mariners) eating-disorder (ED) diagnoses were revealed. Corresponding with the publicizing of each athlete’s ED, all publicly tagged Twitter media using @joey_julius, Joey Julius, @MMarjama, and Mike Marjama were collected using Netlytic software and analyzed. Text analysis revealed that the conversation was supportive and focused on feelings and size. Social network analysis, based on 5 network properties, showed that Joey Julius invoked a larger conversation but that both athletes’ conversations were single sided. Athlete advocacy on social media should be further explored, as it may contribute to changing societal opinion regarding social issues such as EDs.

scholarly journals Brand influence online: cross-platform social network analysis on the enablement of effective brand communities

2021 ◽  
Author(s):  
Cameron Munro
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Methodological Approach ◽  
Brand Community ◽  
Brand Communities ◽  
Online Brand Community ◽  
Network Properties ◽  
Cross Platform

This paper aims to provide a systematic methodological approach for online brand community assessment across multiple social networking platforms. Analysis of influential brands was conducted utilizing a social network analysis (SNA) perspective. Brand communities were scored based on network properties and content analysis. Background research provided a framework of recommended community enablement strategies to determine what type of content and approach is most conducive to brand community proliferation. Based on network analysis and on congruency of following academically suggested community enablement triggers and behavioural dimensions, it was determined that the most effective brand at enabling community across all platforms within the study was Yeti Coolers. Instagram was the focal platform providing engaging content to be shared across networks

The association of spatial configuration with social network for elderly in social housing

2017 ◽  
Vol 29 (3) ◽  
pp. 405-416
Author(s):  
Joo Young Kim ◽  
Young Ook Kim
Keyword(s):  
Social Network ◽  
Social Housing ◽  
Elderly Population ◽  
Spatial Configuration ◽  
The Elderly ◽  
Spatial Network ◽  
Apartment Complex ◽  
The Social ◽  
Network Properties

This study aimed to investigate the association of spatial configuration with social interaction for elderly. A social housing in Seoul was selected for the case study. Using space syntax and social network analysis, the association was examined statistically. This research employed an integration indicator which is most closely related to space use pattern. Questionnaire and interview surveys were conducted to illustrate the pattern of social network. Using the collected data, NetMiner was utilized to conduct a quantitative analysis. Degree, closeness and betweenness indicators were employed to measure relationships in these networks and between individuals. The characteristics of the association established by the statistical analysis between spatial network of housing estate and social network of elderly were discussed. Our results show that spatial network properties can explain characteristics of social network. The accessibility of residential spaces for elderly individuals in social housing apartment complex has an effect on the strength of the social network with neighbours. Also, analysis of the spatial configuration accessibility for the elderly population with integration values has illustrated that the result was opposite to the general theory that ‘the locations with high accessibility could foster more interactions’. Our findings have suggested that we can have a better knowledge to foster more social network among elderly by planning improved spatial network.

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