Analytics and visualization of citation network applying topic-based clustering

Abstract

Survey of papers is not an easy task for novice researchers because it may happen that they miss appropriate keywords for their survey. It often takes a long time for young researchers to find research papers even when they use famous search engines like Google Scholar. In addition, they may not be familiar with understanding positions of papers in their research fields smoothly. To resolve this problem, many researchers have studied citation network visualization techniques for surveying papers. However, it is still often difficult to observe the complicated relations across multiple research fields or traverse the entire relations in their interest. Additional clues, as well as a citation network, are therefore important for survey of papers. In this paper, we proposed a visualization technique for citation networks applying a topic-based paper clustering. Our technique categorizes papers applying LDA (latent dirichlet allocation) and constructs clustered networks consisting of the papers. We applied the technique to three datasets. The results of our visualization technique demonstrated that the proposed technique could contribute to help users to understand the positions of papers in the research fields. We conducted subjective evaluation compared with time-oriented technique and demonstrated that our technique was more helpful for novice researchers like students to find papers.