Bibliometric visualization using

VOSviewer

Ludo Waltman

Centre for Science and Technology Studies (CWTS), Leiden University

Research Output & Impact – New Tools and Concepts

Technical University of Denmark

September 14, 2017

Centre for Science and Technology

Studies (CWTS)

• Research center at Leiden University

focusing on science and technology

studies

• Strong emphasis on bibliometric and

scientometric research

• Provider of commercial scientometric

services

• History of more than 25 years

• Currently about 30 staff members

1

Scientometric databases at CWTS

• Web of Science

• Scopus

• PATSTAT

• PubMed

• CrossRef

• ORCID

• Mendeley

• Altmetric.com

• DataCite

• Full-text databases

(Elsevier, PubMed Central,

Springer, Wiley)

2

3

Outline

• VOSviewer

– Introduction

– Visualizing the large-scale structure of science

– New data sources

• CitNetExplorer

• Principles and challenges for scientometric visualization

4

Most work presented today

was done jointly with CWTS

colleague Nees Jan van Eck

• Any type of (scientometric)

network

• Time dimension is of limited

importance

• Restricted to small and medium-

sized networks

• Only direct citation networks of

publications

• Time dimension is of key

importance

• Support for large networks

5

VOSviewer CitNetExplorer

VOSviewer:

Introduction

6

VOSviewer

7

Co-authorship map

8

Citation map

9

Term co-occurrence map

10

Growing use of VOSviewer

11

VOSviewer:

Visualizing the

large-scale

structure of

science

12

Identifying the structure of science

• Publications in Web of Science are clustered based on

direct citation links (Waltman & Van Eck, 2012):

– About 20 million publications in the period 2000–2016

– Over 300 million citation links

• Clustering is performed using the smart local moving

algorithm (Waltman & Van Eck, 2013), an improvement

of the well-known Louvain algorithm

• The Leiden algorithm, to be released soon, offers further

improvements, allowing all publications in Web of

Science to be clustered within one hour

13

Louvain vs. Leiden

14

Leiden algorithmLouvain algorithm

4000 micro-level fields of science

15

Social

sciences and

humanities

Biomedical

and health

sciences

Physical

sciences and

engineering

Mathematics and

computer science

Life and earth

sciences

Cold vs. hot topics

16

Climate change

Obesity

Complex networks

Activity of Yonsei University

17

Relative strengths of Yonsei University

18

Relative strengths of Korea University

19

20

Scientometrics

Relative strengths of Yonsei University:

Zooming in on social sciences

Focus of Yonsei within scientometrics

21

Focus of Leiden within scientometrics

22

CWTS Leiden Ranking

23

Field-normalized

indicators based on 4000

micro-level fields

VOSviewer: New

data sources

24

New data sources

• Full-text data

• Crossref (I4OC)

• Microsoft Academic

• oaDOI

• ...

25

Full-text data: Availability of Elsevier full-

text publications

26

27

Term co-occurrence map based

on meta data (i.e., titles and

abstracts)

Term co-occurrence

map based on full-

text data

Initiative for Open Citations

28

Journal co-citation map based on

Crossref data for Scientometrics

29

Co-authorship map based on Crossref

data for Journal of Informetrics

30

VOSviewer version 1.6.6 to be released

within the coming weeks!

31

CitNetExplorer

32

CitNetExplorer

33

Standing on the shoulders of giants...

34

Citation network of scientometrics

publications

35

Literature reviewing using CitNetExplorer

36

Principles and

challenges for

scientometric

visualization

Principles for scientometric visualization

1. Acknowledge different use cases of visualizations

(providing insight vs. attracting attention)

2. Adjust visualizations to the mode of presentation (static

vs. interactive)

3. Find an appropriate balance between technical

sophistication and methodological transparency

Principles for scientometric visualization

4. Be aware that visualizations tend to give a simplified

and incomplete representation of the underlying data

5. Combine visualizations with other pieces of evidence,

including your own intuitive judgment

6. Test sensitivity of visualizations to methodological

choices; handle these choices pragmatically

Challenges for scientometric

visualization

• How to take advantage of new scientometric data

sources?

• How to better link interactive visualizations to the

underlying scientometric data?

• How to better handle large scientometric data sets?

• How to improve visualization literacy in scientometrics?

41

Thank you for your attention!