Performance Analysis of Selected Data Mining Algorithms on Social Network Data and Discovery of User Latent Behavior

2015 ◽  
pp. 383-393
Author(s):  
Santosh Phulari ◽  
Parag Bhalchandra ◽  
Santosh Khamitkar ◽  
Nilesh Deshmukh ◽  
Sakharam Lokhande ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Performance Analysis ◽  
Network Data ◽  
Social Network Data ◽  
Data Mining Algorithms ◽  
Mining Algorithms

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.    

A Conceptual Framework for Social Network Data Security

2013 ◽  
pp. 58-86 ◽  
Author(s):  
Sanur Sharma ◽  
Vishal Bhatnagar
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Conceptual Framework ◽  
Social Networking Sites ◽  
Community Analysis ◽  
Network Data ◽  
Data Mining Techniques ◽  
Social Network Data ◽  
The Social ◽  
Using Data

In recent times, there has been a tremendous increase in the number of social networking sites and their users. With the amount of information posted on the public forums, it becomes essential for the service providers to maintain the privacy of an individual. Anonymization as a technique to secure social network data has gained popularity, but there are challenges in implementing it effectively. In this chapter, the authors have presented a conceptual framework to secure the social network data effectively by using data mining techniques to perform in-depth social network analysis before carrying out the actual anonymization process. The authors’ framework in the first step defines the role of community analysis in social network and its various features and temporal metrics. In the next step, the authors propose the application of those data mining techniques that can deal with the dynamic nature of social network and discover important attributes of the social network. Finally, the authors map their security requirements and their findings of the network properties which provide an appropriate base for selection and application of the anonymization technique to protect privacy of social network data.

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