TY - GEN
T1 - Link propagation
T2 - 9th SIAM International Conference on Data Mining 2009, SDM 2009
AU - Kashima, Hisashi
AU - Kato, Tsuyoshi
AU - Yamanishi, Yoshihiro
AU - Sugiyama, Masashi
AU - Tsuda, Koji
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=73449109177&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=73449109177&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:73449109177
SN - 9781615671090
T3 - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics
SP - 1093
EP - 1104
BT - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133
Y2 - 30 April 2009 through 2 May 2009
ER -