In this paper we describe a system for visualizing and analyzing worldwide research topics, rtopmap . We gather data from google scholar academic research profiles, putting together a weighted topics graph, consisting of over 35,000 nodes and 646,000 edges. The nodes correspond to self-reported research topics, and edges correspond to co-occurring topics in google scholar profiles. The rtopmap system supports zooming panning searching and other google-maps-based interactive features. With the help of map overlays, we also visualize the strengths and weaknesses of different academic institutions in terms of human resources (e.g., number of researchers in different areas), as well as scholarly output (e.g., citation counts in different areas). Finally, we also visualize what parts of the map are associated with different academic departments, or with specific documents (such as research papers, or calls for proposals). The system itself is available at this http URL .
Words from paper titles have also been used as indicators for the content of a research topic, and visualizations based on this approach have been studied @cite_21 @cite_55 @cite_7 . Many earlier approaches focus on analyzing specific journals, conferences, or research areas, e.g., analyzing computer science conferences and journals @cite_55 , trends in computer science research @cite_29 , the International Conference on Data Mining (ICDM) @cite_13 , publications in data visualization @cite_15 . Domenico @cite_22 quantify attractive topics (i.e., topics that attract researchers from different areas). Sun @cite_51 build a network, with computer science conferences as nodes and edges between two conferences with common authors. Map-based visualization has been used for document visualization @cite_1 @cite_18 .
@cite_18 @cite_22 @cite_7 @cite_29 @cite_55 @cite_21 @cite_1 @cite_51 @cite_15 @cite_13
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A cartographic approach to mapping nongeographic information helps to manage graphic complexity in visualizations. It aids domain comprehension by forcing us to use the same cognitive skills we use when viewing geographic maps. The author presents a distinctly cartographic approach to mapping nongeographic information. Focusing on the text content of a set of conference abstracts, we can derive 2D visualizations of information spaces that address complexity and automation. Academic research is driven by several factors causing different disciplines to act as “sources” or “sinks” of knowledge. However, how the flow of authors’ research interests – a proxy of human knowledge – evolved across time is still poorly understood. Here, we build a comprehensive map of such flows across one century, revealing fundamental periods in the raise of interest in areas of human knowledge. We identify and quantify the most attractive topics over time, when a relatively significant number of researchers moved from their original area to another one, causing what we call a “diaspora of the knowledge” towards sinks of scientific interest, and we relate these points to crucial historical and political events. Noticeably, only a few areas – like Medicine, Physics or Chemistry – mainly act as sources of the diaspora, whereas areas like Material Science, Chemical Engineering, Neuroscience, Immunology and Microbiology or Environmental Science behave like sinks. Research in Computer Science (CS) evolves rapidly in a dynamic fashion. New research area may emerge and attract researchers, while older areas may have lesser interest from researchers. Studying how trends evolve in CS can be interesting from several dimensions. Furthermore, it can be used to craft research agendas. In this paper, we present trend analysis on research area in CS. We also look at citation trend analysis. Our analysis is performed using the Microsoft Academic Graph dataset. We propose the FoS score to measure the level of interest in any particular research area or topic. We apply the FoS score to investigate general publication trends, citation trends, evolution of research areas, and relation between research areas in CS. Mapping of science and technology can be done at different levels of aggregation, using a variety of methods. In this paper, we propose a method in which title words are used as indicators for the content of a research topic, and cited references are used as the context in which words get their meaning. Research topics are represented by sets of papers that are similar in terms of these word-reference combinations. In this way we use words without neglecting differences and changes in their meanings. The method has several advantages, such as high coverage of publications. As an illustration we apply the method to produce knowledge maps of information science. The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language. The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback. The trends in a research field, especially changes in the features over the years, are subjects of interest for many researchers. This paper reports an exploratory analysis of the changes of research topics in an academic field. The target data of the analysis are the author-keywords included in papers presented at a series of academic conferences, IEEE International Conference on Data Mining (ICDM). The analysis process consists of three phases: (1) frequency of keywords, (2) appearance of keywords in papers, and (3) relationships among keywords. In phase 1, bar charts were used to observe the ranking of frequencies. In phases 2 and 3, anchored maps were adopted. The anchored maps are based on the spring-embedder model, but they provide viewpoints by using fixed "anchors." The analysis process revealed the major topics in the field of data mining and some changes in the relationships among topics.