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- W2078790410 abstract "We propose a novel probabilistic topic model that jointly models authors, documents, cited authors, and venues simultaneously in one integrated framework, as compared to previous work which embeds fewer components. This model is designed for three typical applications in academic network analysis: the problems of expert ranking, cited author prediction and venue prediction. Experiments based on two real world data sets demonstrate the model to be effective, and it outperforms several state-of-the-art algorithms in all three applications." @default.
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- W2078790410 date "2013-08-25" @default.
- W2078790410 modified "2023-09-23" @default.
- W2078790410 title "Academic network analysis" @default.
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- W2078790410 doi "https://doi.org/10.1145/2492517.2492524" @default.
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