Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction.

We propose Link Propagation as a new semi-supervised learning method for link prediction problems, where the task is to predict unknown parts of the network structure by using auxiliary information such as node similarities. Since the proposed method can fill in missing parts of tensors, it is applicable to multi-relational domains, allowing us to handle multiple types of links simultaneously. We also give a novel efficient algorithm for Link Propagation based on an accelerated conjugate gradient method. Keywords Link Prediction, Supervised network inference, Semisupervised learning, Biological networks, Social networks.

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama, Koji Tsuda
• Keywords: Computer Science, Keywords Link Prediction, Link Prediction Problems, SDM 2009, Semi-supervised Learning Method