Erjia Yan

Institutional interactions: Exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks

The objective of this research is to examine the interaction of institutions, based on their citation and collaboration networks. The domain of library and information science is examined, using data from 1965–2010. A linear model is formulated to explore the factors that are associated with institutional citation behaviors, using the number of citations as the dependent variable, and the number of collaborations, physical distance, and topical distance as independent variables. It is found that institutional citation behaviors are associated with social, topical, and geographical factors. Dynamically, the number of citations is becoming more associated with collaboration intensity and less dependent on the country boundary and/or physical distance. This research is informative for scientometricians and policy makers.

Community-based topic modeling for social tagging

Exploring community is fundamental for uncovering the connections between structure and function of complex networks and for practical applications in many disciplines such as biology and sociology. In this paper, we propose a TTR-LDA-Community model which combines the Latent Dirichlet Allocation model (LDA) and the Girvan-Newman community detection algorithm with an inference mechanism. The model is then applied to data from Delicious, a popular social tagging system, over the time period of 2005-2008. Our results show that 1) users in the same community tend to be interested in similar set of topics in all time periods; and 2) topics may divide into several sub-topics and scatter into different communities over time. We evaluate the effectiveness of our model and show that the TTR-LDA-Community model is meaningful for understanding communities and outperforms TTR-LDA and LDA models in tag prediction.

A recursive field-normalized bibliometric performance indicator: an application to the field of library and information science

Two commonly used ideas in the development of citation-based research performance indicators are the idea of normalizing citation counts based on a field classification scheme and the idea of recursive citation weighing (like in PageRank-inspired indicators). We combine these two ideas in a single indicator, referred to as the recursive mean normalized citation score indicator, and we study the validity of this indicator. Our empirical analysis shows that the proposed indicator is highly sensitive to the field classification scheme that is used. The indicator also has a strong tendency to reinforce biases caused by the classification scheme. Based on these observations, we advise against the use of indicators in which the idea of normalization based on a field classification scheme and the idea of recursive citation weighing are combined.

A bird's-eye view of scientific trading: Dependency relations among fields of science

We use a trading metaphor to study knowledge transfer in the sciences as well as the social sciences. The metaphor comprises four dimensions: (a) Discipline Self-dependence, (b) Knowledge Exports/Imports, (c) Scientific Trading Dynamics, and (d) Scientific Trading Impact. This framework is applied to a dataset of 221 Web of Science subject categories. We find that: (i) the Scientific Trading Impact and Dynamics of materials science and transportation science have increased; (ii) biomedical disciplines, physics, and mathematics are significant knowledge exporters, as is statistics and probability; (iii) in the social sciences, economics, business, psychology, management, and sociology are important knowledge exporters; and (iv) Discipline Self-dependence is associated with specialized domains which have ties to professional practice (e.g., law, ophthalmology, dentistry, oral surgery and medicine, psychology, psychoanalysis, veterinary sciences, and nursing).

Entitymetrics: measuring the impact of entities.

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.

Predicting and recommending collaborations: An author-, institution-, and country-level analysis

This study examines collaboration dynamics with the goal to predict and recommend collaborations starting from the current topology. Author-, institution-, and country-level collaboration networks are constructed using a ten-year data set on library and information science publications. Different statistical approaches are applied to these collaboration networks. The study shows that, for the employed data set in particular, higher-level collaboration networks (i.e., country-level collaboration networks) tend to yield more accurate prediction outcomes than lower-level ones (i.e., institution- and author-level collaboration networks). Based on the recommended collaborations of the data set, this study finds that neighbor-information-based approaches are more clustered on a 2-D multidimensional scaling map than topology-based ones. Limitations of the applied approaches on sparse collaboration networks are also discussed.

Finding knowledge paths among scientific disciplines

This paper uncovers patterns of knowledge dissemination among scientific disciplines. Although the transfer of knowledge is largely unobservable, citations from one discipline to another have been proven to be an effective proxy to study disciplinary knowledge flow. This study constructs a knowledge‐flow network in which a node represents a Journal Citation Reports subject category and a link denotes the citations from one subject category to another. Using the concept of shortest path, several quantitative measurements are proposed and applied to a knowledge‐flow network. Based on an examination of subject categories in Journal Citation Reports, this study indicates that social science domains tend to be more self‐contained, so it is more difficult for knowledge from other domains to flow into them; at the same time, knowledge from science domains, such as biomedicine‐, chemistry‐, and physics‐related domains, can access and be accessed by other domains more easily. This study also shows that social science domains are more disunified than science domains, because three fifths of the knowledge paths from one social science domain to another require at least one science domain to serve as an intermediate. This work contributes to discussions on disciplinarity and interdisciplinarity by providing empirical analysis.

Overlaying communities and topics: an analysis on publication networks

Two layers of enriched information are constructed for communities: a paper-to-paper network based on shared author relations and a paper-to-paper network based on shared word relations. k-means and VOSviewer, a modularity-based clustering technique, are used to identify publication clusters in the two networks. Results show that a few research topics such as webometrics, bibliometric laws, and language processing, form their own research community; while other research topics contain different research communities, which may be caused by physical distance.

Research dynamics, impact, and dissemination: A topic-level analysis

In informetrics, journals have been used as a standard unit to analyze research impact, productivity, and scholarship. The increasing practice of interdisciplinary research challenges the effectiveness of journal‐based assessments. The aim of this article is to highlight topics as a valuable unit of analysis. A set of topic‐based approaches is applied to a data set on library and information science publications. Results show that topic‐based approaches are capable of revealing the research dynamics, impact, and dissemination of the selected data set. The article also identifies a nonsignificant relationship between topic popularity and impact and argues for the need to use both variables in describing topic characteristics. Additionally, a flow map illustrates critical topic‐level knowledge dissemination channels.

Research dynamics: Measuring the continuity and popularity of research topics

Dynamic development is an intrinsic characteristic of research topics. To study this, this paper proposes two sets of topic attributes to examine topic dynamic characteristics: topic continuity and topic popularity. Topic continuity comprises six attributes: steady, concentrating, diluting, sporadic, transforming, and emerging topics; topic popularity comprises three attributes: rising, declining, and fluctuating topics. These attributes are applied to a data set on library and information science publications during the past 11 years (2001–2011). Results show that topics on “web information retrieval”, “citation and bibliometrics”, “system and technology”, and “health science” have the highest average popularity; topics on “h-index”, “online communities”, “data preservation”, “social media”, and “web analysis” are increasingly becoming popular in library and information science.