94,452 research outputs found
Constructing bibliometric networks: A comparison between full and fractional counting
The analysis of bibliometric networks, such as co-authorship, bibliographic
coupling, and co-citation networks, has received a considerable amount of
attention. Much less attention has been paid to the construction of these
networks. We point out that different approaches can be taken to construct a
bibliometric network. Normally the full counting approach is used, but we
propose an alternative fractional counting approach. The basic idea of the
fractional counting approach is that each action, such as co-authoring or
citing a publication, should have equal weight, regardless of for instance the
number of authors, citations, or references of a publication. We present two
empirical analyses in which the full and fractional counting approaches yield
very different results. These analyses deal with co-authorship networks of
universities and bibliographic coupling networks of journals. Based on
theoretical considerations and on the empirical analyses, we conclude that for
many purposes the fractional counting approach is preferable over the full
counting one
Scientometrics: Untangling the topics
Measuring science is based on comparing articles to similar others. However,
keyword-based groups of thematically similar articles are dominantly small.
These small sizes keep the statistical errors of comparisons high. With the
growing availability of bibliographic data such statistical errors can be
reduced by merging methods of thematic grouping, citation networks and keyword
co-usage.Comment: 2 pages, 2 figure
Betweenness and Diversity in Journal Citation Networks as Measures of Interdisciplinarity -- A Tribute to Eugene Garfield --
Journals were central to Eugene Garfield's research interests. Among other
things, journals are considered as units of analysis for bibliographic
databases such as the Web of Science (WoS) and Scopus. In addition to
disciplinary classifications of journals, journal citation patterns span
networks across boundaries to variable extents. Using betweenness centrality
(BC) and diversity, we elaborate on the question of how to distinguish and rank
journals in terms of interdisciplinarity. Interdisciplinarity, however, is
difficult to operationalize in the absence of an operational definition of
disciplines, the diversity of a unit of analysis is sample-dependent. BC can be
considered as a measure of multi-disciplinarity. Diversity of co-citation in a
citing document has been considered as an indicator of knowledge integration,
but an author can also generate trans-disciplinary--that is,
non-disciplined--variation by citing sources from other disciplines. Diversity
in the bibliographic coupling among citing documents can analogously be
considered as diffusion of knowledge across disciplines. Because the citation
networks in the cited direction reflect both structure and variation, diversity
in this direction is perhaps the best available measure of interdisciplinarity
at the journal level. Furthermore, diversity is based on a summation and can
therefore be decomposed, differences among (sub)sets can be tested for
statistical significance. In an appendix, a general-purpose routine for
measuring diversity in networks is provided
Using noun phrases extraction for the improvement of hybrid clustering with text- and citation-based components. The example of âInformation Systems Researchâ
The hybrid clustering approach combining lexical and link-based similarities suffered for a long time from the different properties of the underlying networks. We propose a method based on noun phrase extraction using natural language processing to improve the measurement of the lexical component. Term shingles of different length are created form each of the extracted noun phrases. Hybrid networks are built based on weighted combination of the two types of similarities with seven different weights. We conclude that removing all single term shingles provides the best results at the level of computational feasibility, comparability with bibliographic coupling and also in a community detection application
Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns
Many investigations of scientific collaboration are based on statistical
analyses of large networks constructed from bibliographic repositories. These
investigations often rely on a wealth of bibliographic data, but very little or
no other information about the individuals in the network, and thus, fail to
illustrate the broader social and academic landscape in which collaboration
takes place. In this article, we perform an in-depth longitudinal analysis of a
relatively small network of scientific collaboration (N = 291) constructed from
the bibliographic record of a research center involved in the development and
application of sensor network and wireless technologies. We perform a
preliminary analysis of selected structural properties of the network,
computing its range, configuration and topology. We then support our
preliminary statistical analysis with an in-depth temporal investigation of the
assortative mixing of selected node characteristics, unveiling the researchers'
propensity to collaborate preferentially with others with a similar academic
profile. Our qualitative analysis of mixing patterns offers clues as to the
nature of the scientific community being modeled in relation to its
organizational, disciplinary, institutional, and international arrangements of
collaboration.Comment: Scientometrics (In press
On Fractional Approach to Analysis of Linked Networks
In this paper, we present the outer product decomposition of a product of
compatible linked networks. It provides a foundation for the fractional
approach in network analysis. We discuss the standard and Newman's
normalization of networks. We propose some alternatives for fractional
bibliographic coupling measures
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