Conference Paper

Algorithms for Data Retrieval from Online Social Network Graphs

Dept. of Comput., Univ. of Bradford, Bradford, UK
DOI: 10.1109/CIT.2010.293 Conference: 10th IEEE International Conference on Computer and Information Technology, CIT 2010, Bradford, West Yorkshire, UK, June 29-July 1, 2010
Source: DBLP


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.

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Available from: Daniel Neagu, Feb 16, 2014
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    • "In this case the probability values are chosen values between [0] [1] which add up to 1. The attribute fullname and neighbourhood feature number_of_friends are given higher weights because fullname is a common attribute used in identity theft. "
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