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- W2783179986 abstract "This paper studies relation prediction in heterogeneous information networks under PU learning context. One of the challenges of this problem is the imbalance of data number between the positive set P (the set of node pairs with the target relation) and the unlabeled set U (the set of node pairs without the target relation). We propose a K-means and voting mechanism based technique SemiPUclus to extract the reliable negative set RN from U under a new relation prediction framework PURP. The experimental results show that PURP achieves better performance than comparative methods in DBLP co-authorship network data." @default.
- W2783179986 created "2018-01-26" @default.
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- W2783179986 date "2017-11-01" @default.
- W2783179986 modified "2023-09-26" @default.
- W2783179986 title "A relation prediction method based on PU learning" @default.
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- W2783179986 doi "https://doi.org/10.1109/iske.2017.8258752" @default.
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