Deep Learning Based Link Prediction with Social Pattern and External Attribute Knowledge in Bibliographic Networks

The problem of predicting links for information entities is an important task in network analysis. In this regard, link prediction between authors in bibliographic networks has attracted much attention. However, most of these works only center around exploiting network topology features to do prediction, and other factors affecting link formation are rarely considered. In this paper, we introduce two kinds of novel features based on social pattern and external attribute knowledge (SPEAK), then integrate the SPEAK features and topological features into a deep learning framework using deep neural networks (DNNs). We present the performance based on a real world academic social network from AMiner. Experimental results demonstrate that the SPEAK features can significantly boost the link prediction performance especially when potential links span large geodesic distance. In addition, these features are helpful in understanding the mechanisms behind the link formation.