Investigation of the correlation between researchers'properties and productivity through analyzing coauthorship networks
Fuyuki Yoshikane*
National Institution for Academic Degrees and University Evaluation, 1-29-1 Gakuennishimachi, Kodaira, Tokyo, 187-8587, Japan.
Tel.: +81-42-353-1824; Fax: +81-42-353-1863; E-mail: fuyuki@niad.ac.jp

Takafumi Suzuki
University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
E-mail: qq16116@iii.u-tokyo.ac.jp

Keita Tsuji
University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan.
E-mail keita@slis.tsukuba.ac.jp* Corresponding author
Theme: Theme 1; Theme 3
Keywords: co-authorship network; research collaboration; research productivity; performance
prediction

1 BackgroundWe investigate and clarify which properties of a researcher, including the importance incollaboration networks, have strong relationships with his own productivity or that of hiscollaborators. Our analysis serves following purposes; to identify factors affecting
productivity, and to grasp the characteristics of research domains in terms of knowledge
production. Such information is also useful in more practical applications; for example,
when a researcher searches his partners in research collaboration, and when one judges
the substitutability between performance indices in research evaluation.
A large number of studies have analyzed the correlation of properties between authors
and their co-authors [e.g., Kundra and Kretschmer, 1999; Bahr and Zemon, 2000]. Also,
Yasuda [2004] reported that there is a positive correlation between the productivity orcitation ratio of a researcher and the importance in the research collaboration network.
These studies mainly examined the synchronic correlation between researchers'properties. On the other hand, the diachronic correlation of properties, that is, thecorrelation between their subsequent and precedent activity, has not yet been sufficiently
studied using quantitative methods. One of a few exceptions is Yoshikane et al. [2007],
who analyzed the correlation between the researcher's productivity subsequent to acollaboration and the collaborator's precedent activity, targeting only newcomers andtheir senior collaborators. We extend their methodology and analyze the correlation
between researchers' properties and each of the following four aspects of productivity: (1)productivity of themselves in the same period, (2) subsequent productivity of themselves,(3) their collaborators' productivity in the same period, and (4) their collaborators'
subsequent productivity.

2 Methodology
Data
Our investigation targeted the domain of computer science. We used SCI and regarded
the 37 journals classified in the category of "computer science, theory & methods", as thecore journals in computer science. The number of the object researchers in ourinvestigation, who have published in those journals at least one co-authored paper
between 1996 and 2000, is 13,059. In order to grasp the object researchers' and their coauthors' properties (i.e., the productivity of papers and the importance in the network)
during a given period (1996–2000) and during the subsequent period (2001–2005), we
extracted from SCI the papers published over the ten-year-period.

Indices
We divided the data into two periods (1996–2000 and 2001–2005), and calculated thecorrelation coefficients r between (1) researchers' productivity and importance in thecollaboration network in the first period and (2) the productivity, in the same period (i.e.,
the first period) or in the subsequent period (i.e., the second period), of the researchers
themselves or of their collaborators who have published co-authored papers with themduring the first period.

Table 1 Indices for measuring researchers' properties
Second period
First period

subsequent to theircollaboration

objecttheirobject

theirresearchers

collaborators

researchers

collaborators

normal count

NORr1

NORc1

NORr2

NORc2

adjusted count

ADJr1

ADJc1

ADJr2

ADJc2

straight count

STRr1

STRc1

STRr2

STRc2

importanceindegree

DIr1

DIc1

DIr2

DIc2

in theoutdegree

DOr1

DOc1

DOr2

DOc2(leader)

CLr1

CLc1

CLr2

CLc2(cooperator)

CFr1

CFc1

CFr2

CFc2

productivity

collaboration

importance considering

networkglobal structures

To measure the importance in the network, we developed directed graphs where theties are oriented from secondary authors to the first author for each paper. As basic
indices considering only direct ties, we used indegree and outdegree. In addition, CL and
CF proposed by Yoshikane et al. [2006] were adopted as indices of the importance in theglobal structure including indirect ties. Table 1 shows the list of the indices used in thisstudy. Subscripts attached to the indices signify the first ("1") or second ("2") period, andobject researchers ("r") or their collaborators ("c").

3 Resultls and conclusion
Some of the indices representing researchers' properties were highly correlated with theirown productivity in the same period or that of their collaborators. For instance, thecorrelation coefficient between CFr1 (i.e., the importance of researchers as the cooperator
reflecting the global structures of co-authorship networks) and STRc1 (the productivity of
their collaborators as the first author) was about 0.7.
In contrast, for subsequent productivity, we found no index with a similarly highcorrelation coefficient. However, CLr1 was relatively highly correlated with collaborators'
subsequent productivity, when we focused on the relationships between object
researchers and "their cooperators", that is, the collaborators who have published coauthored papers with them "as the secondary author". In particular, CLr1 was highly
correlated with NORc2 (by the normal count) and ADJc2 (by the adjusted count) (r>0.5).
On the other hand, there was little correlation between indegree DIr1 of researchers andthe subsequent productivity of their cooperators. The correlation coefficients of DIr1 withNORc2 (by the normal count), ADJc2 (by the adjusted count), or STRc2 (by the straightcount) are no more than around 0.15. DI, as well as CL, is an index that measures theimportance as the leader in the collaboration network but it only takes into account directties between researchers. The results imply that, if we are to predict, on the basis ofproperties of a researcher, the subsequent productivity of his cooperators, we should take
into account the network's global structure.

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