Active Learning in Link Prediction for social networks

This is just a draft version for the project idea and it is in a partner hunting state.

Members

Troy Hua And I am looking for partners.

Idea

In most online social networks, link prediction is often used as a friend suggestion that users make confirmation to a list of friend suggestions from the system. The feedback of users can be very informative as it sometimes tells the bias of this user, especially temporal bias or interest. And this scenario is similar to an active learning framework that the system can query the user by providing a suggestion and learn from the feedback. We can also look at this feedback in a learning-to-rank model that users' confirmation on the suggestions as a click event on the search results. Active learning in link prediction intuitively also should work well in a cold start situation due to lack of information and active learning can quickly converge and learn more about the user by learning from informative suggestions feedback.

Data sets

Stanford data sets and a lot of social network data from datamob, which has a 2009 facebook social graph data.

Evaluation Metrics

Evaluation is an interesting topic in social network link prediction. One common way is to divide the graph by different timestamps and train on an early graph and test the prediction on a later one. This sounds natural, however, sometimes and especially in a new social network, the sequence of adding friends is just based on the how the system gives the suggestion rather than some real features we can capture. And we should have a more suitable way to deal with our active learning framework.