Chaomei Chen

Interacting with hypertext: a meta-analysis of experimental studies

The meta-analysis compared and synthesized the results of 23 experimental studies on hypertext. The analysis was based on 56 pairs of effect sizes and significance levels of the impact of users, tasks, and tools on interactions with hypertext. This analysis focused on three factors that prevailingly influence the use of hypertext: the cognitive styles and spatial ability of users; the complexity of tasks; and the structure of information organization and the visualization of the structure. The meta-analysis found that this group of experimental studies reported significantly discrepant findings, indicating that substantial differences exist among individual experiments. Individual differences in cognition did not yield enough evidence to conclude that the effect sizes are significantly apart from zero. The meta-analysis showed that the overall performance of hypertext users tended to be more effective than that of nonhypertext users, but the differences in efficiency measures were consistently in favor of nonhypertext users. Users benefited more from hypertext tools for open tasks. Overall, the complexity of tasks has the largest combined effect sizes. Graphical maps that visualize the organization of hypertext have significant impact on the usefulness of a hypertext system. This meta-analysis raised two issues concerned with the present hypertext literature: (a) the absence of a taxonomy of tasks for analyzing and comparing hypertext usability across studies, and (b) the weaknesses of the connections between abstract hypertext reference models and specific hypertext systems. These weaknesses may considerably undermine the significance of individual findings on hypertext usability. Results of the meta-analysis suggest that the discrepancies among empirical findings are related to these weaknesses. Future work on hypertext usability should emphasize task taxonomies along with longitudinal and ethnographic studies for a deep understanding of the interactions between users and hypertext. Recommended research issues for the future are highlighted in Section 5.

visualising semantic spaces and author co-citation networks in digital libraries

Abstract This paper describes the development and application of visualisation techniques for users to access and explore information in a digital library effectively and intuitively. Salient semantic structures and citation patterns are extracted from several collections of documents, including the ACM SIGCHI Conference Proceedings (1995–1997) and ACM Hypertext Conference Proceedings (1987–1998), using Latent Semantic Indexing and Pathfinder Network Scaling. The unique spatial metaphor leads to a natural combination of search and navigation within the same semantic space in a 3-dimensional virtual world. Author co-citation patterns are visualised through a number of author co-citation maps in attempts to reveal the structure of the hypertext, including an overall co-citation map of 367 authors and three periodical maps. These maps highlight predominant research areas in the field. This approach provides a means for transcending the boundaries of collections of documents and visualising more profound patterns in terms of semantic structures and co-citation networks.

Top 10 unsolved information visualization problems

The top 10 unsolved problems list described in this article is a revised and extended version of information visualization problems. These problems are not necessarily imposed by technical barriers, rather, they are problems that might hinder the growth of information visualization as a field. The first three problems highlight issues from a user-centered perspective. The fifth, sixth, and seventh problems are technical challenges in nature. The last three are the ones that need tackling at the disciplinary level. The author broadly defines information visualization as visual representations of the semantics, or meaning, of information. In contrast to scientific visualization, information visualization typically deals with nonnumeric, nonspatial, and high-dimensional data.

The Structure and Dynamics of Co Citation Clusters: A Multiple Perspective Co-Citation Analysis.

A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation clusters members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple- perspective method increases the interpretability and accountability of both ACA and DCA networks.

Visualizing a knowledge domain's intellectual structure

To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co-citation analysis by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape.

Empirical studies of information visualization

A meta-analysis is conducted on a set of empirical studies of information visualization. To be included in the meta-analysis, a study must meet a set of selection criteria. The meta-analysis synthesizes significant levels and effect sizes, tests the heterogeneity of findings from individual studies included and tests the linear trends over a range of information visualization features with ascending visual-spatial complexity. Recommendations for future experimental studies of information visualizations are included.

The Growth of Scientific Knowledge

This is a book about mapping scientific frontiers. We hear of a body of knowledge, research fronts, and scientific frontiers. Scientific frontiers are where one would expect to find not only the cutting-edge knowledge and technology of humans, but also unsolved mysteries, controversies, battles and debates, and revolutions. For example, a bimonthly newsletter Scientific Frontiers 1 digests scientific reports of scientific anomalies: observations and facts that do not quite fit into prevailing scientific theories. This is where the unknown manifests itself in all sorts of ways. The questions addressed in this book concern the dynamics of scientific frontiers and ways that may enable us to understand better the science in the making. In this book, we will take you through our quest to visualize the growth of scientific knowledge. This is not a technical tutorial; instead, the focus is on principles of visual thinking and the ways that may vividly reveal the dynamics of scientific frontiers.

Towards an Explanatory and Computational Theory of Scientific Discovery

We propose an explanatory and computational theory of transformative discoveries in science. The theory is derived from a recurring theme found in a diverse range of scientific change, scientific discovery, and knowledge diffusion theories in philosophy of science, sociology of science, social network analysis, and information science. The theory extends the concept of structural holes from social networks to a broader range of associative networks found in science studies, especially including networks that reflect underlying intellectual structures such as co-citation networks and collaboration networks. The central premise is that connecting otherwise disparate patches of knowledge is a valuable mechanism of creative thinking in general and transformative scientific discovery in particular. In addition, the premise consistently explains the value of connecting people from different disciplinary specialties. The theory not only explains the nature of transformative discoveries in terms of the brokerage mechanism but also characterizes the subsequent diffusion process as optimal information foraging in a problem space. Complementary to epidemiological models of diffusion, foraging-based conceptualizations offer a unified framework for arriving at insightful discoveries and optimizing subsequent pathways of search in a problem space. Structural and temporal properties of potentially high-impact scientific discoveries are derived from the theory to characterize the emergence and evolution of intellectual networks of a field. Two Nobel Prize winning discoveries, the discovery of Helicobacter pylori and gene targeting techniques, and a discovery in string theory demonstrated such properties. Connections to and differences from existing approaches are discussed. The primary value of the theory is that it provides not only a computational model of intellectual growth, but also concrete and constructive explanations of where one may find insightful inspirations for transformative scientific discoveries.

Visualizing evolving networks: minimum spanning trees versus pathfinder networks

Network evolution is an ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualizations of co-citation networks of scientific publications derived by two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and pathfinder networks (PFNETs). Our primary goal is to identify the strengths and weaknesses of the two methods in fulfilling the need for visualizing evolving networks. Two criteria are derived for assessing visualizations of evolving networks in terms of topological properties and dynamical properties. We examine the animated visualization models of the evolution of botulinum toxin research in terms of its co-citation structure across a 58-year span (1945-2002). The results suggest that although high-degree nodes dominate the structure of MST models, such structures can be inadequate in depicting the essence of how the network evolves because MST removes potentially significant links from high-order shortest paths. In contrast, PFNET models clearly demonstrate their superiority in maintaining the cohesiveness of some of the most pivotal paths, which in turn make the growth animation more predictable and interpretable. We suggest that the design of visualization and modeling tools for network evolution should take the cohesiveness of critical paths into account.

Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace

Introduction: Regenerative medicine involves research in a number of fields and disciplines such as stem cell research, tissue engineering and biological therapy in general. As research in these areas advances rapidly, it is critical to keep abreast of emerging trends and critical turns of the development of the collective knowledge. Areas covered: A progressively synthesized network is derived from 35,963 original research and review articles that cite 3875 articles obtained from an initial topic search on regenerative medicine between 2000 and 2011. CiteSpace is used to facilitate the analysis of the intellectual structure and emerging trends. Expert opinion: A major ongoing research trend is concerned with finding alternative reprogramming techniques as well as refining existing ones for induced pluripotent stem cells (iPSCs). A more recent emerging trend focuses on the structural and functional equivalence between iPSCs and human embryonic stem cells and potential clinical and therapeutic implications on regenerative medicine in a long run. The two trends overlap in terms of what they cite, but they are distinct and have different implications on future research. Visual analytics of the literature provides a valuable, timely, repeatable and flexible approach in addition to traditional systematic reviews so as to track the development of new emerging trends and identify critical evidence.