An Effective Algorithm designed for Ranking Research Papers based on the Citation Network

Abstract

In this paper we introduce a novel and efficient approach to detect and rank topics in a large corpus of research papers. With rapidly growing size of academic literature, the problem of topic detection and topic ranking has become a challenging task. We present a unique approach that uses closed frequent keyword-set to form topics. We devise a modified time independent Page Rank algorithm that assigns an authoritative score to each topic by considering the sub-graph in which the topic appears, producing a ranked list of topics. The use of citation network and the introduction of time invariance in the topic ranking algorithm reveal very interesting results. Our approach also provides a clustering technique for the research papers using topics as similarity measure. We extend our algorithms to study various aspects of topic evolution which gives interesting insight into trends in research areas over time. Our algorithms also detect hot topics and landmark topics over the years. We test our algorithms on the DBLP dataset and show that our algorithms are fast, effective and scalable. We have introduced a new metric in the algorithm which takes into account the time factor in ranking there search papers to reduce the bias against the recent papers which get less time for being studied and consequently cited by the researchers as compared to the older papers. Often a researcher is more interested in finding the top conferences in a particular year rather than the overall conference ranking.