TY - GEN
T1 - Co-author relationship prediction in heterogeneous bibliographic networks
AU - Sun, Yizhou
AU - Barber, Rick
AU - Gupta, Manish
AU - Aggarwal, Charu C.
AU - Han, Jiawei
PY - 2011
Y1 - 2011
N2 - The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, i.e., only one type of objects (author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects (e.g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called PathPredict, i.e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that meta path-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.
AB - The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, i.e., only one type of objects (author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects (e.g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called PathPredict, i.e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that meta path-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.
UR - http://www.scopus.com/inward/record.url?scp=80052710349&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052710349&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2011.112
DO - 10.1109/ASONAM.2011.112
M3 - Conference contribution
AN - SCOPUS:80052710349
SN - 9780769543758
T3 - Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
SP - 121
EP - 128
BT - Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
T2 - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
Y2 - 25 July 2011 through 27 July 2011
ER -