Search Machine Learning Repository: @inproceedings{icml2014c2_wu14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks},
    Url = {http://jmlr.org/proceedings/papers/v32/wu14.pdf},
    Abstract = {We study the target node prediction problem: given two social networks, identify those nodes/users from one network (called the source network) who are likely to join another (called the target network, with nodes called target nodes). Although this problem can be solved using existing techniques in the field of cross domain classification, we observe that in many real-world situations the cross-domain classifiers perform sub-optimally due to the heterogeneity between source and target networks that prevents the knowledge from being transferred. In this paper, we propose learning the consistent behavior of common users to help the knowledge transfer. We first present the Consistent Incidence Co-Factorization (CICF) for identifying the consistent users, i.e., common users that behave consistently across networks. Then we introduce the Domain-UnBiased (DUB) classifiers that transfer knowledge only through those consistent users. Extensive experiments are conducted and the results show that our proposal copes with heterogeneity and improves prediction accuracy.},
    Author = {Shan-hung Wu and Hao-heng Chien and Kuan-hua Lin and Philip Yu},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {298-306}
   }