A text visualization method for cross-domain research topic mining

Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations

The IEEE VIS Conference (VIS) recently rebranded itself as a unified conference and officially positioned itself within the discipline of Data Science. Driven by this movement, we investigated (1) who contributed to VIS, and (2) where VIS stands in the scientific world. We examined the authors and fields of study of 3,240 VIS publications in the past 32 years based on data collected from OpenAlex and IEEE Xplore, among other sources. We also examined the citation flows from referenced papers (i.e., those referenced in VIS) to VIS, and from VIS to citing papers (i.e., those citing VIS). We found that VIS has been becoming increasingly popular and collaborative. The number of publications, of unique authors, and of participating countries have been steadily growing. Both cross-country collaborations, and collaborations between educational and non-educational affiliations, namely "cross-type collaborations", are increasing. The dominance of the US is decreasing, and authors from China are now an important part of VIS. In terms of author affiliation types, VIS is increasingly dominated by authors from universities. We found that the topics, inspirations, and influences of VIS research is limited such that (1) VIS, and their referenced and citing papers largely fall into the Computer Science domain, and (2) citations flow mostly between the same set of subfields within Computer Science. Our citation analyses showed that award-winning VIS papers had higher citations. Interactive visualizations, replication data, source code and supplementary material are available at https://32vis.hongtaoh.com and https://osf.io/zkvjm.

Research topic flows in co-authorship networks

In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that Topic Flow Networks are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

Galex: Exploring the Evolution and Intersection of Disciplines

Revealing the evolution of science and the intersections among its sub-fields is extremely important to understand the characteristics of disciplines, discover new topics, and predict the future. The current work focuses on either building the skeleton of science, lacking interaction, detailed exploration and interpretation or on the lower topic level, missing high-level macro-perspective. To fill this gap, we design and implement Galaxy Evolution Explorer (Galex), a hierarchical visual analysis system, in combination with advanced text mining technologies, that could help analysts to comprehend the evolution and intersection of one discipline rapidly. We divide Galex into three progressively fine-grained levels: discipline, area, and institution levels. The combination of interactions enables analysts to explore an arbitrary piece of history and an arbitrary part of the knowledge space of one discipline. Using a flexible spotlight component, analysts could freely select and quickly understand an exploration region. A tree metaphor allows analysts to perceive the expansion, decline, and intersection of topics intuitively. A synchronous spotlight interaction aids in comparing research contents among institutions easily. Three cases demonstrate the effectiveness of our system.

Data-driven decision making in graduate students’ research topic selection

Purpose

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.

Design/methodology/approach

This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.

Findings

The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.

Practical implications

These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.

Originality/value

This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.

A Survey on Visual Approaches for Analyzing Scientific Literature and Patents

The increasingly large number of available writings describing technical and scientific progress, calls for advanced analytic tools for their efficient analysis. This is true for many application scenarios in science and industry and for different types of writings, comprising patents and scientific articles. Despite important differences between patents and scientific articles, both have a variety of common characteristics that lead to similar search and analysis tasks. However, the analysis and visualization of these documents is not a trivial task due to the complexity of the documents as well as the large number of possible relations between their multivariate attributes. In this survey, we review interactive analysis and visualization approaches of patents and scientific articles, ranging from exploration tools to sophisticated mining methods. In a bottom-up approach, we categorize them according to two aspects: (a) data type (text, citations, authors, metadata, and combinations thereof), and (b) task (finding and comparing single entities, seeking elementary relations, finding complex patterns, and in particular temporal patterns, and investigating connections between multiple behaviours). Finally, we identify challenges and research directions in this area that ask for future investigations.

Visualization as Seen through its Research Paper Keywords

We present the results of a comprehensive multi-pass analysis of visualization paper keywords supplied by authors for their papers published in the IEEE Visualization conference series (now called IEEE VIS) between 1990-2015. From this analysis we derived a set of visualization topics that we discuss in the context of the current taxonomy that is used to categorize papers and assign reviewers in the IEEE VIS reviewing process. We point out missing and overemphasized topics in the current taxonomy and start a discussion on the importance of establishing common visualization terminology. Our analysis of research topics in visualization can, thus, serve as a starting point to (a) help create a common vocabulary to improve communication among different visualization sub-groups, (b) facilitate the process of understanding differences and commonalities of the various research sub-fields in visualization, (c) provide an understanding of emerging new research trends, (d) facilitate the crucial step of finding the right reviewers for research submissions, and (e) it can eventually lead to a comprehensive taxonomy of visualization research. One additional tangible outcome of our work is an online query tool (http://keyvis.org/) that allows visualization researchers to easily browse the 3952 keywords used for IEEE VIS papers since 1990 to find related work or make informed keyword choices.