scholarly journals SOCIAL NETWORK DATA RETRIEVAL USING SEMANTIC TECHNOLOGY

2017 ◽  
Vol 10 (13) ◽  
pp. 31 ◽  
Author(s):  
Abhilash Srivastav ◽  
Alok Chauhan
Keyword(s):  
Social Network ◽  
Semantic Web ◽  
Web Application ◽  
Data Analytics ◽  
Data Retrieval ◽  
Network Data ◽  
Semantic Technology ◽  
Network Applications ◽  
Social Network Data ◽  
Data Mining Algorithms

Social network data analysis is an important problem due to proliferation of social network applications, amount of data these applications generate and potential of insight based on this big data. The objective of present work is to propose architecture for a semantic web application to facilitate meaningful social network data analytics as well as answering query about concerned ontology. Proposed technique links, on one hand, tools based on semantic technology provided by social network applications with data analytics tools and on the other hand extends this link to ontology authoring tools for further inference.   Results obtained from data analytics tool, results of query on generated ontology and benchmarking of the performance of data analytics tool are shown. It has been observed that a semantic web application utilizing above mentioned tools and technologies is more versatile and flexible and further improvements are possible by applying generic data mining algorithms to the above scenario.    

Exploring the Properties of Online Social Network Data and Their Implications for Consumer Social Data Analytics

2014 ◽  
pp. 210-230
Author(s):  
Yamen Koubaa
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Consumer Behavior ◽  
Online Social Networks ◽  
Data Analytics ◽  
Online Social Network ◽  
Network Data ◽  
Social Data ◽  
Social Network Data ◽  
Consumer Data

The prediction of consumer behavior is largely based on the analysis of consumer data using statistics as a tool for prediction. Thanks to online social networks, large quantities of heterogeneous consumer data are now available at competitive costs. Though they have much in common with conventional data, online social network datasets display several different properties. The exploration of these unique properties is indispensable to insuring the accuracy of predictions and data analytics. This chapter presents online social data, discusses seven properties of online social network data, suggests some analysis tools, and draws implications regarding the use of social data analytics.

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

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.

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