Mining Research Communities in Bibliographical Data

Abstract

Abstract. Extracting information from very large collections of structured, semistructured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities in the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can convey. A bibliographic database is an essential tool for the research community, yet finding and making use of relationships comprised within such a social network is difficult. In this paper we introduce DBconnect, a prototype that exploits the social network coded within the DBLP database by drawing on a new random walk approach to reveal interesting knowledge about the research community and even recommend collaborations.

Analysis of Computer Science Communities Based on DBLP

“…Besides the network property analysis there is an interest in research related to the topic development and distribution in scientific community. Zäine, Chen and Goebel [21] used collaboration network embedded in DBLP to discover topical connections between the network members and eventually use them in a recommendation system. Another investigation connecting topics and co-authors community has been reported in [22].…”

Efficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining

“…From the literature work, it is evident that identify the target customers is essential to retain the customers for ecommerce sites. It is also evident that the better clustering will provide more accurate predication for identifying user's behavioral pattern [4][5]. It is inferred from the research work that the target customer is identified by different clustering techniques.…”

Network analysis of temporal trends in scholarly research productivity

“…In addition, citation graphs provide useful statistical information. For example, a set of journal/proceedings (or authors) can be grouped by using clustering algorithms (Biryukov & Dong, 2010;Zaane, Chen, & Goebel, 2009;Zhou, Ji, Zha, & Giles, 2006). Another branch of previous work focuses on finding the most important journal/proceedings (or authors) in the sense of citation (Nerur, Sikora, Mangalaraj, & Balijepally, 2005;Yan & Lee, 2007).…”