By Topic

Algorithms for Data Retrieval from Online Social Network Graphs

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Abdulrahman, R. ; Dept. of Comput., Univ. of Bradford, Bradford, UK ; Alim, S. ; Neagu, D. ; Ridley, M.

In the last few years, data extraction from online social networks (OSNs) has become more automated. The aim of this study was to extract all friends from MySpace profiles in order to generate a friendship graph. The graph would be analysed to investigate and apply node vulnerability metrics. This research is an extension of our previous work which concentrated on the extraction of top friends but did not investigate the graph or node vulnerability. The graph was generated from the friendship links that were extracted and placed into a repository. From the graph structure and profiles' personal details, vulnerability was calculated to find the most vulnerable node. Results were promising and provided interesting findings. Metric validation highlighted that the graph can be used to infer information that may not be present on the profile. The number of neighbours and the clustering coefficient were two main factors that affect the vulnerability of nodes.

Published in:

Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on

Date of Conference:

June 29 2010-July 1 2010