Visualization of individual's knowledge by analyzing the citation networks

Visual analysis of knowledge domain is an emerging field of study as science is highly dynamic and constantly evolving. Behind the scene, a knowledge domain is formed and contributed by enormous researchers' publications that describe the common subject of the domain. There is large number of significant activities have been carried out to visualize and identify the knowledge domains of research projects, groups and communities. However, the research on visualizing the knowledge structure at individual level is relative inactive. It is difficult to track down the individual's contribution to the subject and the degree of the knowledge they possess. In this paper, we are attempting to visualize the individual's knowledge structure by analyzing the citation and co-authorship relational structures. We try to analyze and map author's documents to the knowledge domains. By mapping the documents to knowledge domain, we obtain the skeleton of knowledge structure of an individual. Then, we apply the visualization technique to present the result. © 2007 IEEE

Analysis and visualization of co-authorship networks for understanding academic collaboration and knowledge domain of individual researchers

This paper proposed a new approach for collecting, analyzing and visualizing co-authoring data of individuals. This approach can be used for understanding the academic collaboration and knowledge domain of individual researchers in a past period through repetitive co-published works. Particularly we extracted the co-authoring data from the DBLP which is one of the largest on-line Computer Science bibliographic databases available on the Internet. To help users to understand the academic collaboration and knowledge domain of individuals, we developed an InterRing visualizer which shows not only the weight of co-authorship of an individual with other researchers in particular academic year, but also the knowledge domain of the individual that was covered by his/her publications published in a past period. © 2006 IEEE

Utilizing scale-free networks to support the search for scientific publications

When searching for scientific publications, users today often rely on search engines such as Yahoo.com. Whereas searching for publications whose titles are known is considered to be an easy task, users who are looking for important publications in research fields they are unfamiliar with face greater diffiulties since few or no indications of a publication’s importance to the respective fields are given. In this paper we investigate the application of the theory of scale-free networks to derive importance indicators for a collection of publications. A tool was developed to support the user in his publication search by visualizing the publications’ importance indicators derived from the number of citations received and the publication’s age as well as visualizing part of the citation network structure. A preliminary user study indicates the utility of our approach and warrants further research in that direction