It is popular nowadays to bring techniques from bibliometrics and scientometrics into the world of digital libraries to explore mechanisms which underlie community development. In this paper we use the DBLP data to investigate the author's scientific career, and analyze some of the computer science communities. We compare them in terms of productivity and population stability, and use these features to compare the sets of top-ranked conferences with their lower ranked counterparts.
Besides the network property analysis there is an interest in research related to the topic development and distribution in scientific community. Z " a ine, Chen and Goebel @cite_1 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 @cite_3 . The work used CiteSeer as a testbed and aimed at getting insight in topic evolution and connection between the researchers and topics.
@cite_1 @cite_3
2139836297 1975594954
Extracting information from very large collections of structured, semi-structured 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. We propose a method for discovering the dependency relationships between the topics of documents shared in social networks using the latent social interactions, attempting to answer the question: given a seemingly new topic, from where does this topic evolve? In particular, we seek to discover the pair-wise probabilistic dependency in topics of documents which associate social actors from a latent social network, where these documents are being shared. By viewing the evolution of topics as a Markov chain, we estimate a Markov transition matrix of topics by leveraging social interactions and topic semantics. Metastable states in a Markov chain are applied to the clustering of topics. Applied to the CiteSeer dataset, a collection of documents in academia, we show the trends of research topics, how research topics are related and which are stable. We also show how certain social actors, authors, impact these topics and propose new ways for evaluating author impact.