Zeyuan Liu

The structure of scientific collaboration networks in Scientometrics

The structure of scientific collaboration networks in scientometrics is investigated at the level of individuals by using bibliographic data of all papers published in the international journal Scientometrics retrieved from the Science Citation Index (SCI) of the years 1978–2004. Combined analysis of social network analysis (SNA), co-occurrence analysis, cluster analysis and frequency analysis of words is explored to reveal: (1) The microstructure of the collaboration network on scientists’ aspects of scientometrics; (2) The major collaborative fields of the whole network and of different collaborative sub-networks; (3) The collaborative center of the collaboration network in scientometrics.

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.

Where are citations located in the body of scientific articles? A study of the distributions of citation locations

We address issues concerning what one may learn from how citation instances are distributed in scientific articles. We visualize and analyze patterns of citation distributions in the full text of 350 articles published in the Journal of Informetrics. In particular, we visualize and analyze the distributions of citations in articles that are organized in a commonly seen four-section structure, namely, introduction, method, results, and conclusions (IMRC). We examine the locations of citations to the groundbreaking h-index paper by Hirsch in 2005 and how patterns associated with citation locations evolve over time. The results show that citations are highly concentrated in the first section of an article. The density of citations in the first section is about three times higher than that in subsequent sections. The distributions of citations to highly cited papers are even more uneven.

Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology

An extended latent Dirichlet allocation (LDA) model is presented in this paper for patent competitive intelligence analysis. After part-of-speech tagging and defining the noun phrase extraction rules, technological words have been extracted from patent titles and abstracts. This allows us to go one step further and perform patent analysis at content level. Then LDA model is used for identifying underlying topic structures based on latent relationships of technological words extracted. This helped us to review research hot spots and directions in subclasses of patented technology in a certain field. For the extension of the traditional LDA model, another institution-topic probability level is added to the original LDA model. Direct competing enterprises’ distribution probability and their technological positions are identified in each topic. Then a case study is carried on within one of the core patented technology in next generation telecommunication technology-LTE. This empirical study reveals emerging hot spots of LTE technology, and finds that major companies in this field have been focused on different technological fields with different competitive positions.

Distribution of Co-Author Pairs’ Frequencies of the Journal of Information Technology

Lotka’s Law originally calculated only the frequencies of single or first authors. However, given the growth and popularity of scientific collaboration, the distribution of co-author pairs’ frequencies should also be considered. Based on the previous researchers’ results, this article will demonstrate that the distribution of co-author pairs’ frequencies can be reflected by a social gestalt and that there are different shapes of this social gestalt. Results for the distribution of co-author pairs in journals are shown for the co-authorship data of the Journal of Information Technology (1994–2007).

Distribution of Co-Author Pairs’Frequencies in Energy Science and Technology

Compared with Lotka’s law, H. Kretschmer has referred to that the distribution of co-author pairs’ frequencies could be ideally reflected by a social gestalt. Among the former studies, small data sets from one journal and large data from several journals were investigated to verify these assumptions. In the present study, we will examine the data sets from a certain scientific subject “Energy Science and Technology” and its subfield “Solar Energy”, in order to testify that different configurations of social gestalt could reflect the characteristics of the cooperation in social networks, and meanwhile the data size as one of the factor will be proved, which could cause the convex or concave configurations of social gestalt.

International Collaboration Networks of Chinese Scientometrics

In the present study, we try to identify the structure of scientific collaboration networks in scientometrics by using bibliographic data of all papers published by Chinese scientometricians in the international journal Scientometrics retrieved from the Science Citation Index (SCI) of the years 1978–2007. The study is based on bibliographic data retrieved from the Web of Science. The data contains all types of documents published by Chinese scientometricians in Scientometrics during 1978 to 2007.Combined analysis of social network analysis (SNA) and cooccurrence analysis is explored to reveal: (1) The structure of the international collaboration network of Chinese scientometrics; (2) The major collaborative fields of the whole network and of different collaborative sub-networks; (3) The collaborative center of the collaboration network in Chinese scientometrics; (4) The difference of the structure of international collaborative networks between China and India.

A Bibliometric Analysis of Science Cooperation Group Size

Abstract The paper analyzes the applicability of Weibull Density Distribution and Kernel Density Estimation in research of scientific productivity and collaboration group size. The datasets from five categories were retrieved and downloaded from ISI, and they are used to verify Kernel Density Estimation curve fits well with the Weibull Density Distribution curve. As a comparatively convenient function, Kernel Density Estimation can be used as a model to study the relationship between scientific productivity and group size. The best group size and reasonable group size are suggested through Kernel Density Estimation model. We assumed that the best group size in scientific cooperation is positive to discipline biology connotation, however, the result didn’t show such correlation obviously.