A Visualization of Research Papers Based on the Topics and Citation Network

Survey of research papers is not an easy task for novice researchers, because they are not always good at finding all appropriate keywords for the survey. Moreover, it is not easy for them to understand positions of papers in their research fields instantly, even when they use famous search engines like Google Scholar, it may often take a long time for them to find scholarly literature. On the other hand, many researchers have presented citation visualization techniques for surveying research papers. However, it is still often difficult to observe the complicated relations across multiple research fields or traverse the entire relations in their interest. In this paper, we proposed a visualization technique for citation networks applying topic-based paper clustering. Our technique categorizes papers applying LDA (Latent Dirichlet Allocation), and constructs clustered networks consisting of the papers.