Multi-Task Metric Learning on Network Data

Multi-task learning (MTL) has been shown to improve prediction performance in a number of different contexts by learning models jointly on multiple different, but related tasks. In this paper, we propose to do MTL on general network data, which provide an important context for MTL. We first show that MTL on network data is a common problem that has many concrete and valuable applications. Then, we propose a metric learning approach that can effectively exploit correlation across multiple tasks and networks. The proposed approach builds on structural metric learning and intermediate parameterization, and has efficient an implementation via stochastic gradient descent. In experiments, we challenge it with two common real-world applications: citation prediction for Wikipedia articles and social circle prediction in Google+. The proposed method achieves promising results and exhibits good convergence behavior.