Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction
Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama and Koji Tsuda
In: 2009 SIAM International Conference on Data Mining, 30 Apr - 02 May 2009, Sparks, USA.
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.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Koji Tsuda|
|Deposited On:||13 March 2009|