When will it happen?: relationship prediction in heterogeneous information networks
Proceedings of the fifth ACM international conference on Web search and data mining
Journal of the American Society for Information Science and Technology
Mining heterogeneous information networks: the next frontier
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Query-driven discovery of semantically similar substructures in heterogeneous networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge from interconnected data: a heterogeneous information network analysis approach
Proceedings of the VLDB Endowment
User guided entity similarity search using meta-path selection in heterogeneous information networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Evolution of social-attribute networks: measurements, modeling, and implications using google+
Proceedings of the 2012 ACM conference on Internet measurement conference
Coauthor prediction for junior researchers
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Link Prediction in a Modified Heterogeneous Bibliographic Network
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Community evolution detection in time-evolving information networks
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Research-insight: providing insight on research by publication network analysis
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Mining heterogeneous information networks: a structural analysis approach
ACM SIGKDD Explorations Newsletter
Recommending collaborators using keywords
Proceedings of the 22nd international conference on World Wide Web companion
HeteroMF: recommendation in heterogeneous information networks using context dependent factor models
Proceedings of the 22nd international conference on World Wide Web
sonLP: social network link prediction by principal component regression
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the 17th International Database Engineering & Applications Symposium
Proceedings of the 2013 KDD Cup 2013 Workshop
Transferring heterogeneous links across location-based social networks
Proceedings of the 7th ACM international conference on Web search and data mining
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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\emph{Path Predict}, \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 metapath-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.