Network Analysis and Indicators

Milojević, Staša

doi:10.1007/978-3-319-10377-8_3

Cited by 17 publications

(24 citation statements)

References 55 publications

(52 reference statements)

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“…In our experience, providing a simple explanation of the interpretation of non-integer link weights can be challenging. In the VOSviewer software developed by two of us (Van Eck & Waltman, 2010, 2014, bibliometric networks can be constructed using either full or fractional counting, and we have the experience that users of the software sometimes have difficulties with the interpretation of non-integer link weights.…”

Section: Which Counting Methods Should Be Preferred?mentioning

confidence: 99%

“…The co-authorship networks were constructed following the calculations discussed in Subsection 2.3. The VOSviewer software(Van Eck & Waltman, 2010, 2014 was used to create visualizations of the full and fractional counting co-authorship networks.Figures 5 and 6 present visualizations of the university co-authorship networks constructed using full and fractional counting, respectively. Each circle represents a university.…”

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confidence: 99%

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Constructing bibliometric networks: A comparison between full and fractional counting

Perianes‐Rodríguez

Waltman

Eck

2016

Journal of Informetrics

853

411

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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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Section: Which Counting Methods Should Be Preferred?mentioning

confidence: 99%

mentioning

confidence: 99%

Constructing bibliometric networks: A comparison between full and fractional counting

Perianes‐Rodríguez

Waltman

Eck

2016

Journal of Informetrics

853

411

View full text Add to dashboard Cite

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“…(). According to Milojević () “the most common type of scientific and scholarly network is a citation network” (p. 74). In this study, networks are generated based on Mendeley data, which are similar to citation networks.…”

Section: Methodsmentioning

confidence: 99%

“…Powerful computers have led to the generation of comprehensive visualizations by bibliometric researchers, which provide interesting insights into scientific activities. Visualized networks based on bibliometric data may uncover patterns and relations (Milojević, ). For example, scientific collaboration can be studied by investigating networks of coauthorship relations (Cimenler, Reeves, & Skvoretz, ).…”

Section: Introductionmentioning

confidence: 99%

Overlay maps based onMendeley data: The use of altmetrics for readership networks

Bornmann

Haunschild

2015

Asso for Info Science & Tech

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Visualization of scientific results using networks has become popular in scientometric research. We provide base maps for Mendeley reader count data using the publication year 2012 from the Web of Science data. Example networks are shown and explained. The reader can use our base maps to visualize other results with the VOSViewer. The proposed overlay maps are able to show the impact of publications in terms of readership data. The advantage of using our base maps is that it is not necessary for the user to produce a network based on all data (e.g., from 1 year), but can collect the Mendeley data for a single institution (or journals, topics) and can match them with our already produced information. Generation of such large-scale networks is still a demanding task despite the available computer power and digital data availability. Therefore, it is very useful to have base maps and create the network with the overlay technique.

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“…co-occurrence relations between words in abstracts of papers published in information science) (van Eck & Waltman, 2014). Powerful computers have led to the analysis of large networks, which may include the whole Web of Science (WoS) database from Thomson Reuters (Milojević, 2014). Today, these networks are not only of interest for specialists in bibliometrics or networking, but also for stakeholders from publishers, research institutions, and funding agencies.…”

Section: Introductionmentioning

confidence: 99%

Networks of reader and country status: an analysis of Mendeley reader statistics

Haunschild

Bornmann

Leydesdorff

2015

PeerJ Computer Science

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The number of papers published in journals indexed by the Web of Science core collection is steadily increasing. In recent years, nearly two million new papers were published each year; somewhat more than one million papers when primary research papers are considered only (articles and reviews are the document types where primary research is usually reported or reviewed). However, who reads these papers? More precisely, which groups of researchers from which (self-assigned) scientific disciplines and countries are reading these papers? Is it possible to visualize readership patterns for certain countries, scientific disciplines, or academic status groups? One popular method to answer these questions is a network analysis. In this study, we analyze Mendeley readership data of a set of 1,133,224 articles and 64,960 reviews with publication year 2012 to generate three different networks: (1) The network based on disciplinary affiliations of Mendeley readers contains four groups: (i) biology, (ii) social sciences and humanities (including relevant computer sciences), (iii) bio-medical sciences, and (iv) natural sciences and engineering. In all four groups, the category with the addition "miscellaneous" prevails. (2) The network of co-readers in terms of professional status shows that a common interest in papers is mainly shared among PhD students, Master's students, and postdocs. (3) The country network focusses on global readership patterns: a group of 53 nations is identified as core to the scientific enterprise, including Russia and China as well as two thirds of the OECD (Organisation for Economic Cooperation and Development) countries.

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Network Analysis and Indicators

Abstract: Networks have for a long time been used both as a metaphor and as a method for studying science. With the advent of very large data sets and the increase in the computational power, network analysis became more prevalent in

Cited by 17 publications

References 55 publications

Constructing bibliometric networks: A comparison between full and fractional counting

Constructing bibliometric networks: A comparison between full and fractional counting

Overlay maps based onMendeley data: The use of altmetrics for readership networks

Networks of reader and country status: an analysis of Mendeley reader statistics

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