Paper (10 pages)
Official IEEE/ACM published version: http://dx.doi.org/10.1145/2492517.2492524 Author's version: PDF (200KB)
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.
In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 324-333, Niagara Falls, Canada, August 2013.
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