Comprehensive Influence Propagation Modelling for Hybrid Social Network

2016 ◽  
pp. 597-608 ◽  
Author(s):  
Weihua Li ◽  
Quan Bai ◽  
Minjie Zhang
Keyword(s):  
Social Network ◽  
Influence Propagation ◽  
Propagation Modelling

scholarly journals A Correlative Scrutiny for Improving the Career Guidance Links in Social Network

2019 ◽  
Vol 9 (1) ◽  
pp. 1466-1470
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Career Development ◽  
Network Analysis ◽  
Clustering Algorithm ◽  
Career Guidance ◽  
Effective Interaction ◽  
Professional Behaviors ◽  
Influence Propagation ◽  
Development Framework

Social Network analysis techniques have shown the ability to meet educators and influence career development guidance. Career education mentoring plays an important role in the career supporting which are the interest, the strength, and the aspirations of the students. In this paper, we proposed a career development framework enhances the node influence propagation and effective interaction between nodes is taken as a strong link, from the node influence propagation we divide it into two categories such as: (1) career predictions to persuade prospective graduates to pursue the desired career path, with career prospects considered by them as a learning opportunity. (2) Social network analysis and persuasive techniques are used to motivate within a social networking framework where there is a tendency to adopt desired professional behaviors. The process begins with the discovery of behavioral features to create a cognitive profile and to identify individual disabilities. We compare a clustering algorithm that predicts the accuracy values and pattern of creations to a social network for achieving collaborative cognition.

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Learning Conversation and Shared Network Leadership

2010 ◽  
Vol 9 (4) ◽  
pp. 181-194 ◽  
Author(s):  
Jürgen Weibler ◽  
Sigrid Rohn-Endres
Keyword(s):  
Qualitative Research ◽  
Grounded Theory ◽  
Social Network ◽  
Interorganizational Networks ◽  
Original Network ◽  
High Quality ◽  
Qualitative Research Design ◽  
Collective Processes ◽  
Shared Network ◽  
Practical Implications

This paper develops an understanding of how shared leadership emerges in social network interactions. On the basis of a qualitative research design (grounded theory methodology – GTM) our study in two interorganizational networks offers insights into the interplay between structures, individuals, and the collective for the emergence of shared network leadership (SNL). The network-specific Gestalt of SNL appears as a pattern of collective and individual leadership activities unified under the roof of a highly developed learning conversation. More importantly, our findings support the idea that individual network leadership would not emerge without embeddedness in certain high-quality collective processes of relating and dialogue. Both theoretical and practical implications of this original network leadership perspective are discussed.

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