A general method of digital network analysis particularly suitable for use with low-speed computers

1961 ◽  
Vol 108 (41) ◽  
pp. 369 ◽  
Author(s):  
M.N. John
Keyword(s):  
Network Analysis ◽  
Low Speed ◽  
Digital Network ◽  
General Method

scholarly journals Network Analysis in Relationship Dynamics and Digital Social Identity in Youths

2018 ◽  
Vol 13 (1) ◽  
pp. 225-231
Author(s):  
Rajendra Man Banepali ◽  
Subarna Shakya ◽  
Gajendra Sharma
Keyword(s):  
Network Analysis ◽  
Social Identity ◽  
Network Theory ◽  
Digital Technology ◽  
Relationship Dynamics ◽  
Digital Network ◽  
Individual Actor

 People living with digital technology or without technology, no one is immune from digital technology nowadays. Netizen culture became a style of life in youth, the style as new behaviors. Aiming to scrutinize behavior of individual actor of digital network, this study has explored relationship dynamics of digital network observing students in survey. Network theory and cognition theory along with models from various sources have been employed to draw a result from data. Digital social identity and individual network behaviors like pattern of establishing new relation, altering relationship dynamics and termination are result of this work. In future, this model of network analysis can be expanded to various domains like political, intelligence and international relations.Journal of the Institute of Engineering, 2017, 13(1): 225-231

scholarly journals Role-similarity based functional prediction in networked systems: application to the yeast proteome

2005 ◽  
Vol 2 (4) ◽  
pp. 327-333 ◽  
Author(s):  
Petter Holme ◽  
Mikael Huss
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Social Network ◽  
Network Analysis ◽  
Similarity Measures ◽  
Networked Systems ◽  
Functional Prediction ◽  
Yeast Proteome ◽  
Prediction Scheme ◽  
Model Networks ◽  
General Method

We propose a general method to predict functions of vertices where (i) the wiring of the network is somehow related to the vertex functionality and (ii) a fraction of the vertices are functionally classified. The method is influenced by role-similarity measures of social network analysis. The two versions of our prediction scheme are tested on model networks where the functions of the vertices are designed to match their network surroundings. We also apply these methods to the proteome of the yeast Saccharomyces cerevisiae and find the results compatible with more specialized methods.

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