This paper discovers the research themes of institutes’ research work using analysis of scientiﬁc literature. The proposed methodology creates research proﬁles of the institutes y aggregating citations of highly cited works and then clusters the documents that cite those works to determine the impact, in that area of the research. Research themes are identiﬁed by clustering author deﬁed keywords. The approach is demonstrated on several Japanese institutes in the ﬁeld of Nanotechnology. The analytical techniques discussed in this paper can discover niche focus of institutes’ research. This information can be very useful for the research administrators, funding agencies, and institutes leaders to understand the research structure of institutes in order to support resource allocation decisions.
We propose a new methodology to discover the relationship between authors and research domains. The methodology utilizes the classic author-topic model to find the probabilistic relationships among authors, topics and papers. A distance matrix is used to find authors close to the authors obtained from the classic author-topic model for a given topic. In addition, the relationship of selected authors is examined with a co-authorship network model. We compare the performance of our methodology with that of the classic author-topic model. The experimental results on the DBLP database show that the proposed methodology discovers more precise relationships between authors and research domains than does the classic author-topic model, with a 36% increase in performance.