Mapping Science - Mapping Scientific Frontiers: The Quest for Knowledge Visualization - page 256

due to the lack of download and citation data. On the other hand, the sources of
information used in these approaches are indirect to the new ideas reported in
scientific publications. In an analogy, we give credits to an individual based on
his/her credit history instead of assessing the risk of the current transaction directly.
In such approaches, we will not be able to know where precisely the novelty of an
idea is coming from. We will not be able to know whether similar ideas have been
proposed in the past.
Many studies have addressed factors that could explain or even predict future
citations of a scientific publication (Aksnes 2003 ;Hirsch 2007 ; Levitt and Thelwall
2008 ; Persson 2010 ). For example, is a paper's citation count last year a good
predictor for new citations this year? Are the download times a good predictor
of citations? Is it true that the more references a paper cites, the more citations
it will receive later on? Similarly, the potential role of prestige, or the Matthew
Effect coined by Robert Merton, has been commonly investigated, ranging from
the prestige of authors to the prestige of journals in which articles are published
(Dewett and Denisi 2004 ). However, many of these factors are loosely and indirectly
coupled with the conceptual and semantic nature of the underlying subject matter of
concern. We refer them as extrinsic factors. In contrast, intrinsic factors have direct
and profound connections with the intellectual content and structure. One example
of intrinsic factor is concerned with the structural variation of a field of study.
A notable example is the work by Swanson on linking previously disjoint bodies
of knowledge, such as the connection between fish oil and Reynaud's syndrome
(Swanson 1986a ).
Researchers have made various attempts to characterize future citations and
identify emerging core articles (Shibata et al. 2007 ; Walters 2006 ). Shibata et al.
for example, studied citation networks in two subject areas, Gallium Nitride and
Complex Networks, and found that while past citations are a good predictor of near-
future citations, the betweenness centrality is correlated with citations in a longer
term.
Upham et al. ( 2010 ) studied the role of cohesive intellectual communities -
schools of thoughts - in promoting and constraining knowledge creation. They
analyzed publications on management and concluded that it is significantly bene-
ficial for new knowledge to be a part of a school of thought and the most influential
position within a school of thought is in the semi-periphery of the school. In
particular, boundary-spanning research positioned at the semi-periphery of a school
would attract attention from other schools of thought and receive the most citations
overall. Their study used a zero-inflated negative binomial regression (ZINB).
Negative binomial regression models have been used to predict the expected mean
patent citations (Fleming and Bromiley 2000 ). Hsieh ( 2011 ) studied inventions as
a combination of technological features. In particular, the closeness of features
plays an interesting role. Neither overly related nor loosely related features are
good candidates for new inventions. Useful inventions arise with rightly positioned
features where the cost of synthesis is minimized.
Takeda and Kjikawa ( 2010 ) reported three stages of clustering in citation
networks. In the first stage, core clusters are formed, followed by the formation