Fuyuki Yoshikane

Comparative analysis of coauthorship networks of different domains: The growth and change of networks

Many studies have tried to describe patterns of research collaboration through observing coauthorship networks. Those studies mainly analyze static networks, and most of them do not consider the development of networks. In this study, we turn our attention to the development of personal collaboration networks. On the basis of an analysis from two viewpoints, i.e., growth in the number of collaborating partners and change in the relationship strength with partners, we describe and compare the characteristics of four different domains, i.e., electrical engineering, information processing, polymer science, and biochemistry.

An analysis of the connection between researchers’ productivity and their co-authors’ past attributions, including the importance in collaboration networks

Although many studies have analyzed the “synchronic” correlation of properties between authors and their co-authors, the “diachronic” correlation of properties, i.e., the correlation between their subsequent and precedent activity, has not yet been sufficiently studied using quantitative methods. This study pays attention not only to productivity but also the importance in the collaboration network as a measure of the researcher’s activity, and clarifies whether there is any connection between (i) the researcher’s activity subsequent to a collaboration and (ii) the collaborator’s precedent activity, aiming at deriving knowledge about the diachronic effect of collaborators.

A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search

This paper proposes a methodology which discriminates the articles by the target authors (“true” articles) from those by other homonymous authors (“false” articles). Author name searches for 2,595 “source” authors in six subject fields retrieved about 629,000 articles. In order to extract true articles from the large amount of the retrieved articles, including many false ones, two filtering stages were applied. At the first stage any retrieved article was eliminated as false if either its affiliation addresses had little similarity to those of its source article or there was no citation relationship between the journal of the retrieved article and that of its source article. At the second stage, a sample of retrieved articles was subjected to manual judgment, and utilizing the judgment results, discrimination functions based on logistic regression were defined. These discrimination functions demonstrated both the recall ratio and the precision of about 95% and the accuracy (correct answer ratio) of 90–95%. Existence of common coauthor(s), address similarity, title words similarity, and interjournal citation relationships between the retrieved and source articles were found to be the effective discrimination predictors. Whether or not the source author was from a specific country was also one of the important predictors. Furthermore, it was shown that a retrieved article is almost certainly true if it was cited by, or cocited with, its source article. The method proposed in this study would be effective when dealing with a large number of articles whose subject fields and affiliation addresses vary widely.

A method for the comparative analysis of concentration of author productivity, giving consideration to the effect of sample size dependency of statistical measures

In this article, we propose a method for the comparative analysis of concentration in author productivity distributions. We define the notion of concentration on the basis of two viewpoints (absolute and relative concentration) and select G (Ginis index) and V (the number of authors) as suitable measures for these two viewpoints. We then discuss the statistical peculiarity of author productivity data (i.e., most of the statistical measures change systematically according to changes in the sample size) and we explain our method using developmental profiles, which takes into account the sample size dependency of statistical measures. Finally, by applying it to actual data, we demonstrate the usefulness of the proposed method.

Multiple regression analysis of a patent's citation frequency and quantitative characteristics: the case of Japanese patents

Although many studies have been conducted to clarify the factors that affect the citation frequency of “academic papers,” there are few studies where the citation frequency of “patents” has been predicted on the basis of statistical analysis, such as regression analysis. Assuming that a patent based on a variety of technological bases tends to be an important patent that is cited more often, this study examines the influence of the number of cited patents’ classifications and compares it with other factors, such as the numbers of inventors, classifications, pages, and claims. Multiple linear, logistic, and zero-inflated negative binomial regression analyses using these factors are performed. Significant positive correlations between the number of classifications of cited patents and the citation frequency are observed for all the models. Moreover, the multiple regression analyses demonstrate that the number of classifications of cited patents contributes more to the regression than do other factors. This implies that, if confounding between factors is taken into account, it is the diversity of classifications assigned to backward citations that more largely influences the number of forward citations.

Analysis of the relationship between citation frequency of patents and diversity of their backward citations for Japanese patents

Conventional patent citation analyses have focused mainly on the presence of citation relationships, the number of patents cited by the subject patent, and the number of times the subject patent is cited by others (i.e., the numbers of backward and forward citations of the subject patent). However, most of them have not focused on patent classifications. Assuming that a patent based on a variety of technological bases tends to be an important patent that is cited more often, this study examines and clarifies the relationship between the diversity of classifications assigned to backward citations and the number of forward citations for Japanese patents. The results show notable differences in the number of classifications assigned to backward citations between the often cited and less frequently cited groups. It is considered that the diversity of backward citations can be utilized in the evaluation criteria for grouping that roughly identifies the often cited patents or eliminates a large part of less frequently cited patents.

Diversity of fields in patent citations: synchronic and diachronic changes

While there is a large body of research analyzing the overall structure of citation relations for patents, there has been very little research seeking to clarify the characteristics of fields relating to the diffusion of technology through observing the citation network surrounding each patent individually and tracing its growth. This study focused on the classifications assigned to patents and examined the diversity of the fields of patents citing each patent from the following two perspectives: (1) expected values for growth in the number of citing fields, when regarding the observation period as being in a synchronic state and assuming that the strength of connections between each patent and citing fields is constant; and (2) empirical values for growth in the number of citing fields according to the increase in the cumulative number of citations over time. From the results, it was confirmed that the strength of potential connections between each patent and citing fields changes over time. Especially in the fields of “chemistry; metallurgy” and “physics,” the following change is considerable: a patent tends to receive citations repeatedly from a limited range of fields for a while, but later comes to be cited by various fields.

Syllabus database and Web service on higher education

In this paper analyzing a structure of higher education information, we propose syllabus XML database, Web services on education information, and curriculum analyzing system based on advanced communication technology.

DOI Links on Wikipedia

In this paper, we analyzed Digital Object Identifier (DOI) links among English, Japanese, and Chinese Wikipedias (hereafter, enwiki, jawiki, and zhwiki, respectively), which possibly work as a bridge between the Web users and scholarly information. Most of the DOI links in these Wikipedias were revealed to be CrossRef DOIs. The second most-referenced in jawiki were JaLC DOIs, whereas those in zhwiki were ISTIC DOIs. JaLC DOIs were uniquely referenced in jawiki, and ISTIC DOIs tend to be referenced in zhwiki. In terms of DOI prefixes, Elsevier BV was the largest registrant in all languages. Nature Publishing Group and Wiley-Blackwell were also commonly referenced. The content hosted by these registrants was shared among the Wikipedia communities. Moreover, overlapping analysis showed that jawiki and zhwiki share the DOI links with enwiki at a similar high rate. The analysis of revision histories showed that the DOI links had been added to enwiki before they were included in jawiki and zhwiki — indicating that the majority of DOI links in jawiki and zhwiki were added by translating from enwiki. These findings imply that the DOI links in Wikipedia may result in multiple counts of altmetrics.