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We illustrate the model using global citation counts of scientific publications

recorded in the Web of Science. NB models are defined as follows using log as the

link function.

Global citations
Coauthors
C
Modularity Change Rate
C
Cluster Linkage
C

Centrality Divergence
C
References
C
Pages

Global citations
is the dependent variable.
Coauthors
is a factor of three levels of

1, 2, and 3. Level 3 is assigned to articles with three or more coauthors.
Coauthors
is

an indirect indicator of the extent to which an article synthesizes ideas from different

areas of expertise represented by each coauthor.

Three structural variation metrics are included as co-variants in generalized

linear models, namely
Modularity Change Rate
(MCR),
Cluster Linkage
(CL), and

Centrality Divergence
(
C
KL
). According to our theory of creativity, groundbreaking

ideas are expected to cause strong structural variations. If global citation counts

provide a reasonable proxy of recognitions of intellectual contributions in a

scientific community, we would expect that at least some of the structural variation

metrics will have statistically significant main effects on global citations.

The number of cited references and the number of pages are commonly reported

in the literature as good predictors of citations. In order to compare the effects of

structural variation with these commonly reported extrinsic properties of scientific

publications,
References
and
Pages
are included in the models. Our theory offers

a simpler explanation why the more references a paper cites, the more citations it

appears to get. Due to the boundary spanning synthetic mechanism, an article needs

to explain multiple parts and how they can be innovatively connected. This process

will result in citing more references than an article that covers a narrower range of

topics. Review papers by their nature belong to this category.

It is known that articles published earlier tend to have more citations than articles

published later. The exposure time of an article is included in the NB models in

terms of a logarithmically transformed year of publication of an article.

An intuitive way to interpret coefficients in NB models is to use incidence rate

ratios (IRRs) estimated by the models. For example, if
Coauthors
has an IRR of

1.5, it means that as the number of coauthors increases by one the global citation

counts would be expected to increase a factor of 1.5, i.e. increasing 1.5 times, while

holding other variables in the model constant. In our models, we will particularly

examine statistically significant IRRs of structural variation models.

Zero-inflated negative binomial models (ZINB) use the same set of variables.

The count model of ZINB is identical to the NB model described above. The zero-

inflated model of ZINB uses the same set of variables to predict the excessive zeros.

We found little in the literature about good predictors of zeros in a comparable

context. We choose to include all the six variables in the zero-inflated model to

provide a broader view of the zero-generating process. ZINBs are defined as follows:

Global citations
Coauthors
C
Modularity Change Rate
C
Cluster Linkage
C

Centrality Divergence
C
References
C
Pages

Zero citations
Coauthors
C
Modularity Change Rate
C
Cluster Linkage
C

Centrality Divergence
C
References
C
Pages

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