Link prediction in research collaboration: a multi-network representation learning framework with joint training

With the rapid advancement of scientific research, collaboration in this area is becoming increasingly important. One of the major challenges is the link prediction problem for research collaboration. Recently, learning-based link prediction methods have received much attention. However, most of these studies have solely concentrated on exploiting a single network and its topology features for prediction, and ignore other factors that may influence link formation. To address this issue, in this paper we propose a link prediction model based on multi-network representation learning. Specifically, we develop new features based on the author's institutions and published papers, and three networks incorporating these features are modeled. Then, the network representation method based on joint training is proposed to embed the networks in a low-dimensional space. Finally, the authors' feature vectors are combined in finer granularity, and collaboration prediction is performed in a supervised manner. The performance of our model is evaluated by comparing it with other link prediction methods on a real-world dataset, and the experimental results show the effectiveness of our model.