LAUSR

With the explosive growth of academic writing, it is difficult for researchers to find significant papers in their area of interest. In this paper, we propose a pipeline model, named collective topical PageRank, to evaluate the topic-dependent impact of scientific papers. First, we fit the model to a correlation topic model based on the textual content of papers to extract scientific topics and correlations. Then, we present a modified PageRank algorithm, which incorporates the venue, the correlations of the scientific topics, and the publication year of each paper into a random walk to evaluate the paper’s topic-dependent academic impact. Our experiments showed that the model can effectively identify significant papers as well as venues for each scientific topic, recommend papers for further reading or citing, explore the evolution of scientific topics, and calculate the venues’ dynamic topic-dependent academic impact.