An Efficient Algorithm for Topic Ranking and Modeling Topic Evolution

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 keywordset to form topics. We devise a modified time independent PageRank 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.