TY - GEN
T1 - Citation prediction in heterogeneous bibliographic networks
AU - Yu, Xiao
AU - Gu, Quanquan
AU - Zhou, Mianwei
AU - Han, Jiawei
PY - 2012
Y1 - 2012
N2 - To reveal information hiding in link space of biblio- graphical networks, link analysis has been studied from different perspectives in recent years. In this paper, we address a novel problem namely citation prediction, that is: given information about authors, topics, target publication venues as well as time of certain research paper, finding and predicting the citation relationship between a query paper and a set of previous papers. Considering the gigantic size of relevant papers, the loosely connected citation network structure as well as the highly skewed citation relation distribution, citation prediction is more challenging than other link prediction problems which have been studied before. By building a meta-path based prediction model on a topic discrim- inative search space, we here propose a two-phase cita- Tion probability learning approach, in order to predict citation relationship effectively and efficiently. Exper- iments are performed on real-world dataset with com- prehensive measurements, which demonstrate that our framework has substantial advantages over commonly used link prediction approaches in predicting citation relations in bibliographical networks.
AB - To reveal information hiding in link space of biblio- graphical networks, link analysis has been studied from different perspectives in recent years. In this paper, we address a novel problem namely citation prediction, that is: given information about authors, topics, target publication venues as well as time of certain research paper, finding and predicting the citation relationship between a query paper and a set of previous papers. Considering the gigantic size of relevant papers, the loosely connected citation network structure as well as the highly skewed citation relation distribution, citation prediction is more challenging than other link prediction problems which have been studied before. By building a meta-path based prediction model on a topic discrim- inative search space, we here propose a two-phase cita- Tion probability learning approach, in order to predict citation relationship effectively and efficiently. Exper- iments are performed on real-world dataset with com- prehensive measurements, which demonstrate that our framework has substantial advantages over commonly used link prediction approaches in predicting citation relations in bibliographical networks.
UR - http://www.scopus.com/inward/record.url?scp=84880233067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880233067&partnerID=8YFLogxK
U2 - 10.1137/1.9781611972825.96
DO - 10.1137/1.9781611972825.96
M3 - Conference contribution
AN - SCOPUS:84880233067
SN - 9781611972320
T3 - Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
SP - 1119
EP - 1130
BT - Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PB - Society for Industrial and Applied Mathematics Publications
T2 - 12th SIAM International Conference on Data Mining, SDM 2012
Y2 - 26 April 2012 through 28 April 2012
ER -