Finding Latest Influential Research Papers Through Modeling Two Views of Citation Links
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.
Lu Huang Hongyan Liu Jun He Xiaoyong Du
Key Labs of Data Engineering and Knowledge Engineering,Ministry of Education,China School of Informa Department of Management Science and Engineering,Tsinghua University,Beijing 100084,China
国际会议
International Asia-Pacific Web Conference(第18届国际亚太互联网大会)
苏州
英文
555-566
2016-09-23（万方平台首次上网日期，不代表论文的发表时间）