Co-citation Analysis: An Overview

This article gives an overview of co-citation analysis and its applications in tracking the linkages among the intellectual works and mapping the evolutionary structure of scientific disciplines. It also focuses on the features, interface, terminology used, merits and demerits of co-citation based online database applications

Understanding the Impact of Early Citers on Long-Term Scientific Impact

This paper explores an interesting new dimension to the challenging problem of predicting long-term scientific impact (LTSI) usually measured by the number of citations accumulated by a paper in the long-term. It is well known that early citations (within 1-2 years after publication) acquired by a paper positively affects its LTSI. However, there is no work that investigates if the set of authors who bring in these early citations to a paper also affect its LTSI. In this paper, we demonstrate for the first time, the impact of these authors whom we call early citers (EC) on the LTSI of a paper. Note that this study of the complex dynamics of EC introduces a brand new paradigm in citation behavior analysis. Using a massive computer science bibliographic dataset we identify two distinct categories of EC - we call those authors who have high overall publication/citation count in the dataset as influential and the rest of the authors as non-influential. We investigate three characteristic properties of EC and present an extensive analysis of how each category correlates with LTSI in terms of these properties. In contrast to popular perception, we find that influential EC negatively affects LTSI possibly owing to attention stealing. To motivate this, we present several representative examples from the dataset. A closer inspection of the collaboration network reveals that this stealing effect is more profound if an EC is nearer to the authors of the paper being investigated. As an intuitive use case, we show that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins. At the closing, we present an online portal to visualize EC statistics along with the prediction results for a given query paper

Visualization of Publication Impact

Measuring scholarly impact has been a topic of much interest in recent years. While many use the citation count as a primary indicator of a publications impact, the quality and impact of those citations will vary. Additionally, it is often difficult to see where a paper sits among other papers in the same research area. Questions we wished to answer through this visualization were: is a publication cited less than publications in the field?; is a publication cited by high or low impact publications?; and can we visually compare the impact of publications across a result set? In this work we address the above questions through a new visualization of publication impact. Our technique has been applied to the visualization of citation information in INSPIREHEP (http://www.inspirehep.net), the largest high energy physics publication repository

Which cities produce excellent papers worldwide more than can be expected? A new mapping approach--using Google Maps--based on statistical significance testing

The methods presented in this paper allow for a statistical analysis revealing centers of excellence around the world using programs that are freely available. Based on Web of Science data, field-specific excellence can be identified in cities where highly-cited papers were published significantly. Compared to the mapping approaches published hitherto, our approach is more analytically oriented by allowing the assessment of an observed number of excellent papers for a city (in the sample) against the expected number. Using this test, the approach cannot only identify the top performers in output but the "true jewels." These are cities locating authors who publish significantly more top cited papers than can be expected. As the examples in this paper show for physics, chemistry, and psychology, these cities do not necessarily have a high output of excellent papers

Networks of reader and country status: An analysis of Mendeley reader statistics

The number of papers published in journals indexed by the Web of Science core collection is steadily increasing. In recent years, nearly two million new papers were published each year; somewhat more than one million papers when primary research papers are considered only (articles and reviews are the document types where primary research is usually reported or reviewed). However, who reads these papers? More precisely, which groups of researchers from which (self-assigned) scientific disciplines and countries are reading these papers? Is it possible to visualize readership patterns for certain countries, scientific disciplines, or academic status groups? One popular method to answer these questions is a network analysis. In this study, we analyze Mendeley readership data of a set of 1,133,224 articles and 64,960 reviews with publication year 2012 to generate three different kinds of networks: (1) The network based on disciplinary affiliations of Mendeley readers contains four groups: (i) biology, (ii) social science and humanities (including relevant computer science), (iii) bio-medical sciences, and (iv) natural science and engineering. In all four groups, the category with the addition "miscellaneous" prevails. (2) The network of co-readers in terms of professional status shows that a common interest in papers is mainly shared among PhD students, Master's students, and postdocs. (3) The country network focusses on global readership patterns: a group of 53 nations is identified as core to the scientific enterprise, including Russia and China as well as two thirds of the OECD (Organisation for Economic Co-operation and Development) countries.Comment: 26 pages, 6 figures (also web-based startable), and 2 table