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by (Watts and Strogatz 1998 ) at the time of its publication. The article has the
highest scores in Cluster Linkage and C KL scores, 5.43 and 1.14, respectively. The
figure offers a visual confirmation that the article was indeed making boundary-
spanning connections. Recall that the data set was constructed by expanding the
seed article based on forward citation links. These boundary-spanning links provide
empirical evidence that the groundbreaking paper was connecting two groups of
clusters. The emergence of Cluster #8 complex network was the consequence of the
Tab le 8.1 summarizes the results of five NB regression models with different
types of networks. They have an average dispersion parameter ™ of 0.5270, which
is equivalent to an alpha of 1.8975. Coauthors has an average IRR of 1.3278.
References has an average IRR of 1.0126. Pages has an average IRR of 0.9714.
The effects of the three variables are consistent and stable across the five types of
networks. In contrast, the effects of structural variations are less stable. On the other
hand, structural variations appear to have a stronger impact on global citations than
other more commonly studied measures such as Coauthors and References .For
example, CL has an IRR of 3.160 in networks of co-cited references and an IRR of
1.33 10 8 in networks of noun phrases. IRRs that are greater than 1.0 predict an
increase of global citations.
We have found statistical evidence of the boundary-spanning mechanism. An
article that introduces novel connections between clusters of co-cited references is
likely to become highly cited subsequently. In addition, we have found that the
IRRs of Cluster Linkage are more than twice as much as the IRRs of Coauthors
and References . This finding provides a more fundamental explanation of why the
number of references cited by an article appears to be a good predictor of its future
citations as found in many previous studies. As a result, the structural variation
paradigm clarifies why a number of extrinsic features appear to be associated with
high citations.
A distinct characteristic of the structural variation approach is the focus on the
potential connection between the degree of structural variation introduced by an
article and its future impact. The analytic and modeling procedure demonstrated
here is expected to serve as an exemplar for subsequent studies along this line of
research. More importantly, the focus on the underlying mechanisms of scientific
activity is expected to provide additional insights and practical guidance for
scientists, sociologists, historians, and philosophers of scientific knowledge.
There are many new challenges and opportunities ahead. For example, how
common is the boundary-spanning mechanism in scientific discoveries overall?
What are the other major mechanisms and how do they interact with the boundary-
spanning mechanism? There are other potentially valuable techniques that we have
not utilized in the present study, including topic modeling, citation context analysis,
survival analysis and burst detection. In short, a lot of work is to be done and this is
an encouraging start.
Figure 8.5 shows that the structural variation approach is applied to the study
of the potential of patents. The patent US6537746 is ranked high on the structural
variation scale. Its position is marked by a star. The areas where the patent made
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