Presentation at the International Conference on Data-driven Discovery: "When Data Science Meets Information Science", Beijing, China, June 20, 2016.

1. Large-scale analysis of bibliometric
networks
Nees Jan van Eck
Centre for Science and Technology Studies (CWTS), Leiden University
International Conference on Data-driven Discovery:
When Data Science Meets Information Science
Beijing, China, June 20, 2016

2. Bibliographic databases: ‘Big data’
1
Web of Science Scopus
Journals 12,000 20,000
Publications 45 million 35 million
Citations 1 billion 0.9 billion

3. Bibliometric networks
2
Web of
Science
Scopus
Citation network
of pubs / authors / journals
Co-authorship network
of authors / organizations
Co-citation network
of pubs / authors / journals
Co-occurrence network
of keywords / terms
Bibliographic coupling network
of pubs / authors / journals
Bibliographic
database

4. Outline
• Software tools
• Network analysis techniques
• Analysis of data science
3

5. Software tools
4

6. Software tools
• VOSviewer (www.vosviewer.com)
– Tool for constructing and visualizing bibliometric networks
• CitNetExplorer (www.citnetexplorer.nl)
– Tool for visualizing and analyzing citation networks of
publications
• Both tools have been developed together
with my colleague Ludo Waltman 5

7. VOSviewer
6

8. VOSviewer: Overview
• Software tool for visualizing (bibliometric) networks
• Built-in support for popular bibliographic databases
• Text mining functionality
• Layout and clustering techniques
• Advanced visualization features:
– Smart labeling algorithm
– Overlay visualizations
– Density visualizations (‘heat map’)
• Users:
– Researchers
– Professional users (e.g., universities, libraries, funders,
publishers)
7

9. Map of university co-authorship
network
8

10. Map of journal citation network
9

11. CitNetExplorer
10

12. • Any type of bibliometric
network
• Co-authorship, direct citations,
co-citation, and bibliographic
coupling
• Time dimension is ignored
• Networks of at most ~10,000
nodes are supported
• Only citation networks of
publications
• Direct citation between
publications
• Time dimension is explicitly
considered
• Millions of publications are
supported
11
VOSviewer CitNetExplorer

13. Network
analysis
techniques
12

14. Network analysis techniques
13
Layout:
• Assigning the nodes in a network to
locations in a (usually 2d) space
(a.k.a. mapping)
• Visualization of similarities (VOS)
Clustering:
• Partitioning the nodes in a network
into a number of groups (a.k.a.
community detection)
• Weighted modularity
• Smart local moving algorithm

15. 1414
Clustering can be seen as mapping
in a restricted space

16. 1515
Clustering can be seen as mapping
in a restricted space

17. Unified approach to mapping and
clustering
Minimize
where
n: number of nodes in the network
m: total weight of all edges in the network
Aij: weight of edge between nodes i and j
ki: total weight of all edges of node i
16
 

ji
ij
ji
ijij
ji
n ddA
kk
m
xxQ 2
1
2
),,( 
Mapping
xi: vector denoting the location
of node i in a p-dimensional
space


p
k
jkikjiij xxxxd
1
2
)(
Clustering
xi: integer denoting the
community to which node i
belongs
: resolution parameter






ji
ji
ij
xx
xx
d
if1
if0


18. Smart local moving algorithm
17
Q = 0.4198
Q = 0.3791
Reduced
network
Local moving
heuristic in
subnetworks
Local moving heuristic
Original
network

19. Algorithmically constructed
classification system of science
• 17.8 million publications from the period 2000–
2015 indexed in Web of Science
• 282.4 million citation relations
• Classification system of 3 hierarchical levels:
– 27 broad disciplines
– 817 fields
– 4,113 subfields
18

20. Breakdown of scientific literature into
817 fields
19
Social sciences
and humanitiesBiomedical and
health sciences
Life and earth
sciences
Mathematics and
computer science
Physical
sciences and
engineering

21. Publications in scientometrics
subfield
20

22. Time-line map of highly cited
scientometrics publications
21

23. Analysis of
data science
22

24. What is data science?
• Empirical operationalization of data science based
on publications with ‘data’ in title or abstract
23
Wikipedia: “Data Science is an interdisciplinary field
about processes and systems to extract knowledge
or insights from data … which is a continuation of
some of the data analysis fields such as statistics,
data mining, and predictive analytics”
LCDS: “Data Science … deals with finding, analyzing
and validating complex patterns in data. Data
Science methods are indispensable for maintaining a
competitive edge in all disciplines in science”

25. Growth of data-driven research
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0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
1990 1995 2000 2005 2010 2015
Percentageofpublications
% 'data' publications % 'theory' publications

26. Breakdown of scientific literature into
817 fields
25
Social sciences
and humanitiesBiomedical and
health sciences
Life and earth
sciences
Mathematics and
computer science
Physical
sciences and
engineering

27. Data-driven nature of different
scientific fields
26
Social sciences
and humanitiesBiomedical and
health sciences
Life and earth
sciences
Mathematics and
computer science
Physical
sciences and
engineering
% pub. with ‘data’ in title or abstract

28. Data-driven nature of different
scientific fields
27
artificial
intelligence
statisticsbioinformatics
neuroimaging pattern
recognition
astronomy
earth
water
climate
remote
sensing
nutrition
obesity
addiction
accident
analysis
% pub. with ‘data’ in title or abstract

29. Data science fields (at least 25% ‘data’
publications)
28
Social sciences
and humanitiesBiomedical and
health sciences
Life and earth
sciences
Mathematics and
computer science
Physical
sciences and
engineering

30. Term map of data science fields
29

31. China’s publication output in data
science fields
30
Social sciences
and humanitiesBiomedical and
health sciences
Life and earth
sciences
Mathematics and
computer science
Physical
sciences and
engineering

32. China’s publication output in data
science fields
31
artificial
intelligence
pattern
recognition
high
energy
earth
atmospheres
weather
remote
sensing

33. Chinese institutes with most publications
in data science fields (2011-2015)
• Chinese Academy of Sciences
• Peking University
• Tsinghua University
• China University of Geosciences
• Zhejiang University
• Nanjing University
• Shanghai Jiao Tong University
• University of Science and Technology of China
• Beijing Normal University
• University of Hong Kong
32

34. CAS publication output in data
science fields
33
earth
atmospheres
weather
remote
sensing
vegetation
astronomy
high energy

35. Term map based on CAS publications in
data science fields
34

36. CAS (Beijing Branch) publication
output in data science fields
35
astronomy
earth
atmospheres
weather
remote
sensing
vegetation
high energy

37. CAS (Shanghai Branch) publication
output in data science fields
36
bioinformatics
genetics
astronomy
nuclear

38. Do it yourself!
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www.vosviewer.com www.citnetexplorer.nl

39. Thank you for your attention!
38