Abstract
Finding hidden topics and latest topic influential papers in a corpus can help researchers get a quick overview and recent development of a scientific research field. Existing work focused on finding milestone papers which are usually published many years ago. Finding latest influential papers is a more challenging problem due to lack of enough citation information of newly published papers. In this paper, we study this problem and propose a novel way of modeling citation links with a probabilistic generative model. The key idea is to consider two views of citation, both citing and being cited of each paper. Through this idea, we can not only model topic dependence between cited and citing papers but also incorporate latest papers into our model. Based on these ideas, we jointly model the two views with an extension of topic model, Bi-Citation-LDA model, which can not only find previous important papers but also find newly published influential papers in each topic. Experiments on real dataset and comparison with existing methods indicate that our model can effectively find latest topic influential papers.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China under grant No. 71272029, 71432004, 71490724 and 61472426, the 863 program under grant No. 2014AA015204, and the Beijing Municipal Natural Science Foundation under grant No. 4152026.
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Huang, L., Liu, H., He, J., Du, X. (2016). Finding Latest Influential Research Papers Through Modeling Two Views of Citation Links. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_45
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DOI: https://doi.org/10.1007/978-3-319-45814-4_45
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