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.
Ranking of publication venues is often closely related with important issues such as evaluating the contributions of individual scholars research groups, or subscription decision making. The development of large-scale digital libraries and the availability of various meta data provide the possibility of building new measures more efficiently and accurately. In this work, we propose two novel measures for ranking the impacts of academic venues an easy-to-implement seed-based measure that does not use citation analysis, and a realistic browsing-based measure that takes an article reader's behavior into account. Both measures are computationally efficient yet mimic the results of the widely accepted Impact Factor. In particular, our proposal exploits the fact that: (1)in most disciplines, there are "top" venues that most people agree on; and (2) articles that appeared in good venues are more likely to be viewed by readers. Our proposed measures are extensively evaluated on a test case of the Database research community using two real bibliography data sets - ACM and DBLP. Finally, ranks of venues by our proposed measures are compared against the Impact Factor using the Spearman's rank correlation coefficient, and their positive rank order relationship is proved with a statistical significance test. Acknowledgments in research publications, like citations, indicate influential contributions to scientific work. However, acknowledgments are different from citations; whereas citations are formal expressions of debt, acknowledgments are arguably more personal, singular, or private expressions of appreciation and contribution. Furthermore, many sources of research funding expect researchers to acknowledge any support that contributed to the published work. Just as citation indexing proved to be an important tool for evaluating research contributions, we argue that acknowledgments can be considered as a metric parallel to citations in the academic audit process. We have developed automated methods for acknowledgment extraction and analysis and show that combining acknowledgment analysis with citation indexing yields a measurable impact of the efficacy of various individuals as well as government, corporate, and university sponsors of scientific work. We propose a popularity weighted ranking algorithm for academic digital libraries that uses the popularity factor of a publication venue overcoming the limitations of impact factors. We compare our method with the naive PageRank, citation counts and HITS algorithm, three popular measures currently used to rank papers beyond lexical similarity. The ranking results are evaluated by discounted cumulative gain(DCG) method using four human evaluators. We show that our proposed ranking algorithm improves the DCG performance by 8.5 on average compared to naive PageRank, 16.3 compared to citation count and 23.2 compared to HITS. The algorithm is also evaluated by click through data from CiteSeer usage log. Bibliometrics are important measures for venue quality in digital libraries. Impacts of venues are usually the major consideration for subscription decision-making, and for ranking and recommending high-quality venues and documents. For digital libraries in the Computer Science literature domain, conferences play a major role as an important publication and dissemination outlet. However, with a recent profusion of conferences and rapidly expanding fields, it is increasingly challenging for researchers and librarians to assess the quality of conferences. We propose a set of novel heuristics to automatically discover prestigious (and low-quality) conferences by mining the characteristics of Program Committee members. We examine the proposed cues both in isolation and combination under a classification scheme. Evaluation on a collection of 2,979 conferences and 16,147 PC members shows that our heuristics, when combined, correctly classify about 92 of the conferences, with a low false positive rate of 0.035 and a recall of more than 73 for identifying reputable conferences. Furthermore, we demonstrate empirically that our heuristics can also effectively detect a set of low-quality conferences, with a false positive rate of merely 0.002. We also report our experience of detecting two previously unknown low-quality conferences. Finally, we apply the proposed techniques to the entire quality spectrum by ranking conferences in the collection.