What can social networks on the internet know about persons who are friends of members, but have no user profile of their own? Researchers from the Interdisciplinary Center for Scientific Computing of Heidelberg University studied this question. Their work shows that through network analytical and machine learning tools the relationships between members and the connection patterns to non-members can be evaluated with regards to non-member relationships.
Using simple contact data, it is possible, under certain conditions, to correctly predict that two non-members know each other with approx. 40 per cent probability.
For several years scientists have been investigating what conclusions can be drawn from a computational analysis of input data by applying adequate learning and prediction algorithms. In a social network, information not disclosed by a member, such as sexual orientation or political preferences, can be ''calculated'' with a very high degree of accuracy if enough of his or her friends did provide such information about themselves.
''Once confirmed friendships are known, predicting certain unknown properties is no longer that much of a challenge for machine learning'', says Prof. Dr. Fred Hamprecht, co-founder of the Heidelberg Collaboratory for Image Processing (HCI).
Until now, studies of this type were restricted to users of social networks, i.e. persons with a posted user profile who agreed to the given privacy terms.
''Non-members, however, have no such agreement. We therefore studied their vulnerability to the automatic generation of so-called shadow profiles'', explains Prof. Dr. Katharina Zweig, who until recently worked at the Interdisciplinary Center for Scientific Computing (IWR) of Heidelberg University.