Abstract
Members of social network platforms often choose to reveal private information, and thus sacrifice some of their privacy, in exchange for the manifold opportunities and amenities offered by such platforms. In this article, we show that the seemingly innocuous combination of knowledge of confirmed contacts between members on the one hand and their email contacts to non-members on the other hand provides enough information to deduce a substantial proportion of relationships between non-members. Using machine learning we achieve an area under the (receiver operating characteristic) curve () of at least for predicting whether two non-members known by the same member are connected or not, even for conservative estimates of the overall proportion of members, and the proportion of members disclosing their contacts.
Highlights
Some individuals prefer to keep intimate details such as their political preferences or sexual orientation private
The general pattern is that the prediction accuracy increases with r and a
We found that the prediction accuracy is rather independent of how individuals decide to become a member of an online social network platform, but that some percentage of independent decisions like in the random walk (RW), ego networks selection (EN), and random selection of members (RS) model helps the platform to explore the latent social network more efficiently
Summary
Some individuals prefer to keep intimate details such as their political preferences or sexual orientation private. We show that such an assumption is no longer valid: with the help of machine learning, social network operators can make predictions regarding the acquaintance or lack thereof between two non-members with a high rate of success. To our knowledge these are the first results on the potential of social network platforms to infer relationships between non-members. We present the first link prediction work where learning and testing are performed on entirely independent networks
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
