400 research outputs found
CitNetExplorer: A new software tool for analyzing and visualizing citation networks
We present CitNetExplorer, a new software tool for analyzing and visualizing
citation networks of scientific publications. CitNetExplorer can for instance
be used to study the development of a research field, to delineate the
literature on a research topic, and to support literature reviewing. We first
introduce the main concepts that need to be understood when working with
CitNetExplorer. We then demonstrate CitNetExplorer by using the tool to analyze
the scientometric literature and the literature on community detection in
networks. Finally, we discuss some technical details on the construction,
visualization, and analysis of citation networks in CitNetExplorer
Field-normalized citation impact indicators and the choice of an appropriate counting method
Bibliometric studies often rely on field-normalized citation impact
indicators in order to make comparisons between scientific fields. We discuss
the connection between field normalization and the choice of a counting method
for handling publications with multiple co-authors. Our focus is on the choice
between full counting and fractional counting. Based on an extensive
theoretical and empirical analysis, we argue that properly field-normalized
results cannot be obtained when full counting is used. Fractional counting does
provide results that are properly field normalized. We therefore recommend the
use of fractional counting in bibliometric studies that require field
normalization, especially in studies at the level of countries and research
organizations. We also compare different variants of fractional counting. In
general, it seems best to use either the author-level or the address-level
variant of fractional counting
A systematic empirical comparison of different approaches for normalizing citation impact indicators
We address the question how citation-based bibliometric indicators can best
be normalized to ensure fair comparisons between publications from different
scientific fields and different years. In a systematic large-scale empirical
analysis, we compare a traditional normalization approach based on a field
classification system with three source normalization approaches. We pay
special attention to the selection of the publications included in the
analysis. Publications in national scientific journals, popular scientific
magazines, and trade magazines are not included. Unlike earlier studies, we use
algorithmically constructed classification systems to evaluate the different
normalization approaches. Our analysis shows that a source normalization
approach based on the recently introduced idea of fractional citation counting
does not perform well. Two other source normalization approaches generally
outperform the classification-system-based normalization approach that we
study. Our analysis therefore offers considerable support for the use of
source-normalized bibliometric indicators
A smart local moving algorithm for large-scale modularity-based community detection
We introduce a new algorithm for modularity-based community detection in
large networks. The algorithm, which we refer to as a smart local moving
algorithm, takes advantage of a well-known local moving heuristic that is also
used by other algorithms. Compared with these other algorithms, our proposed
algorithm uses the local moving heuristic in a more sophisticated way. Based on
an analysis of a diverse set of networks, we show that our smart local moving
algorithm identifies community structures with higher modularity values than
other algorithms for large-scale modularity optimization, among which the
popular 'Louvain algorithm' introduced by Blondel et al. (2008). The
computational efficiency of our algorithm makes it possible to perform
community detection in networks with tens of millions of nodes and hundreds of
millions of edges. Our smart local moving algorithm also performs well in small
and medium-sized networks. In short computing times, it identifies community
structures with modularity values equally high as, or almost as high as, the
highest values reported in the literature, and sometimes even higher than the
highest values found in the literature
A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS
VOS is a new mapping technique that can serve as an alternative to the
well-known technique of multidimensional scaling. We present an extensive
comparison between the use of multidimensional scaling and the use of VOS for
constructing bibliometric maps. In our theoretical analysis, we show the
mathematical relation between the two techniques. In our experimental analysis,
we use the techniques for constructing maps of authors, journals, and keywords.
Two commonly used approaches to bibliometric mapping, both based on
multidimensional scaling, turn out to produce maps that suffer from artifacts.
Maps constructed using VOS turn out not to have this problem. We conclude that
in general maps constructed using VOS provide a more satisfactory
representation of a data set than maps constructed using well-known
multidimensional scaling approaches
Counting publications and citations: Is more always better?
Is more always better? We address this question in the context of
bibliometric indices that aim to assess the scientific impact of individual
researchers by counting their number of highly cited publications. We propose a
simple model in which the number of citations of a publication depends not only
on the scientific impact of the publication but also on other 'random' factors.
Our model indicates that more need not always be better. It turns out that the
most influential researchers may have a systematically lower performance, in
terms of highly cited publications, than some of their less influential
colleagues. The model also suggests an improved way of counting highly cited
publications
From Louvain to Leiden: guaranteeing well-connected communities
Community detection is often used to understand the structure of large and
complex networks. One of the most popular algorithms for uncovering community
structure is the so-called Louvain algorithm. We show that this algorithm has a
major defect that largely went unnoticed until now: the Louvain algorithm may
yield arbitrarily badly connected communities. In the worst case, communities
may even be disconnected, especially when running the algorithm iteratively. In
our experimental analysis, we observe that up to 25% of the communities are
badly connected and up to 16% are disconnected. To address this problem, we
introduce the Leiden algorithm. We prove that the Leiden algorithm yields
communities that are guaranteed to be connected. In addition, we prove that,
when the Leiden algorithm is applied iteratively, it converges to a partition
in which all subsets of all communities are locally optimally assigned.
Furthermore, by relying on a fast local move approach, the Leiden algorithm
runs faster than the Louvain algorithm. We demonstrate the performance of the
Leiden algorithm for several benchmark and real-world networks. We find that
the Leiden algorithm is faster than the Louvain algorithm and uncovers better
partitions, in addition to providing explicit guarantees
Universality of citation distributions revisited
Radicchi, Fortunato, and Castellano [arXiv:0806.0974, PNAS 105(45), 17268]
claim that, apart from a scaling factor, all fields of science are
characterized by the same citation distribution. We present a large-scale
validation study of this universality-of-citation-distributions claim. Our
analysis shows that claiming citation distributions to be universal for all
fields of science is not warranted. Although many fields indeed seem to have
fairly similar citation distributions, there are quite some exceptions as well.
We also briefly discuss the consequences of our findings for the measurement of
scientific impact using citation-based bibliometric indicators
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
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