scholarly journals A SYSTEMATIC REVIEW AND SOCIAL NETWORK ANALYSIS OF THE EMERGING DEFINITION OF “DEPRESCRIBING”

2015 ◽  
Vol 55 (Suppl_2) ◽  
pp. 461-461
Author(s):  
Anu Taneja ◽  
Bhawna Gupta ◽  
Anuja Arora

The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.


2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Raglan Maddox ◽  
Rachel Davey ◽  
Ray Lovett ◽  
Anke van der Sterren ◽  
Joan Corbett ◽  
...  

2015 ◽  
Vol 63 (5) ◽  
pp. 566-584 ◽  
Author(s):  
Sung-Heui Bae ◽  
Alexander Nikolaev ◽  
Jin Young Seo ◽  
Jessica Castner

2018 ◽  
Vol 19 (7) ◽  
pp. 976-988 ◽  
Author(s):  
S. Zhang ◽  
K. de la Haye ◽  
M. Ji ◽  
R. An

Author(s):  
Hirotoshi Takeda ◽  
Duane P. Truex ◽  
Michael J. Cuellar ◽  
Richard Vidgen

Following previous research findings, this paper argues that the currently predominant method of evaluating scholar performance - publication counts in “quality” journals - is flawed due to the subjectivity inherent in the generation of the list of approved journals and absence of a definition of quality. Truex, Cuellar, and Takeda (2009) improved on this method by substituting a measurement of “influence” using the Hirsch statistics to measure ideational influence. Since the h-family statistics are a measure of productivity and the uptake of a scholar’s ideas expressed in publications, this methodology privileges the uptake of a scholar’s ideas over the venue of publication. Influence is built through other means than by having one’s papers read and cited. The interaction between scholars resulting in co-authored papers is another way to build scholarly influence. This aspect of scholarly influence, which the authors term social influence, can be assessed by Social Network Analysis (SNA) metrics that examine the nature and strength of coauthoring networks among IS Scholars. The paper demonstrates the method of assessing social influence by analysis of the social network of AMCIS scholars and compares the results of this analysis with other co-authorship networks from the ECIS and ICIS communities.


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