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A learning to rank framework for future friendship ranking in streaming heterogeneous social networks

Friendship prediction is an important aspect of a social network. Social network users rely a lot on friendship suggestion as it helps them in improving their network of friends. Moreover it has a huge impact in analyzing whom one is influenced by. In order to know this information it is not only essential to identify the potential friends, but also rank them to quantify their influence. To do this, we propose a learning to rank framework using the most popular machine learning technique, LambdaMART with a new boosting algorithm. Our framework provides unparalleled values of normalized discounted cumulative gain measure. We also analyze which feature of the social network is helpful in getting these nonpareil values. Our boosting algorithm yields an analytical solution for the over‐fitting problem with the increasing number of iterations, thereby augmenting the robustness of the framework.