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Applications of community detection in
bibliometric network analysis
Nees Jan van Eck
Centre for Science and Technology Studies (CWTS), Leiden University
EURANDOM workshop “Networks with community structure”, Eindhoven
January 24, 2014
Outline
• Bibliometric network analysis at CWTS
• VOSviewer
• Unified approach to visualization and community
detection
• Community detection in large citation networks
• CitNetExplorer

1
Bibliometric
network analysis
at CWTS

2
Bibliometric network analysis at CWTS
• In-house databases:
– Thomson Reuters Web of Science
– Elsevier Scopus

• Bibliometric networks:
– Publication citation networks
– Journal co-citation/bibliographic coupling networks
– Term co-occurrence networks
– Co-authorship networks
– Etc.

• Applications:
– Research institutions: Research assessment
– Scientific publishers: Journal profiling
– Funding agencies: Science policy analyses

3
VOSviewer
(www.vosviewer.com)

4
VOSviewer

(Van Eck & Waltman, Scientometrics, 2010)

5
Citation network of fields in Web of
Science

6
Co-occurrence network of terms in
clinical neurology

7
Unified
approach to
visualization and
community
detection

8
Visualization vs. community detection
• Visualization (‘mapping’):
– Assigning the nodes in a network to locations in a (usually twodimensional) space

• Community detection (‘clustering’):
– Partitioning the nodes in a network into a number of groups

9
Community detection seen as
visualization in a restricted space

10
Community detection seen as
visualization in a restricted space

11
Unified approach to visualization and
community detection
Minimize Q (x 1 , , x n )
i j

2m
Aij d ij2
kik j

d ij
i j

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

Visualization

Community detection
xi: integer denoting the community
to which node i belongs

0
if x i x j
d ij
1
if x i x j
: resolution parameter

xi: vector denoting the location of
node i in a p-dimensional
space
p

d ij

xi

xj

(x ik

x jk )2

k 1

12
Unified approach: Community detection
Equivalent to a weighted variant of modularity-based
community detection (Waltman et al., 2010)

Maximize

ˆ
Q(x 1 , , x n )

1
2m

(x i , x j )w ij Aij
i j

kik j
2m

where
(xi, xj) equals 1 if xi = xj and 0 otherwise
w ij

2m
kik j

13
Unified approach: Visualization
• Equivalent to the VOS (visualization of similarities)
technique (Van Eck & Waltman, 2007)
• Limit case of multidimensional scaling (Van Eck et
al., 2010)
Q
i j

2m
Aij x i
kik j

Wij Dij

xi

xj

2

xi

xj

VOS

i j

xj

2

MDS

i j

Dij

kik j
2m

Aij

1

Wij

2m
Aij
kik j
14
Unified approach
Most commonly used community detection technique
(modularity) and most commonly used visualization
technique (MDS) can be brought together in a unified
framework
Unified
approach

Modularity
(weighted)

VOS

MDS
(limit case)

15
Community
detection in
large citation
networks

16
Classification systems of scientific
publications
• Web of Science/Scopus classification systems:
– Scientific fields defined at the level of journals rather than individual
publications
– Difficulties with multidisciplinary journals
– High level of aggregation

– Sometimes outdated or inaccurate

• Disciplinary classification systems:
– E.g., CA, JEL, MeSH, PACS
– Not available for all disciplines

– Sometimes outdated or inaccurate

17
Algorithmically constructed
classification systems
• Publications (not journals) are clustered into fields based
on citation relations
• Fields are defined at different levels of granularity and
are organized hierarchically

• Community detection based on a variant of the standard
modularity function that accounts for differences in
citation practices across fields
• Optimization using the smart local moving algorithm

18
Example (Waltman & Van Eck, 2012)
• 10.2 million publications from the period 2001–2010
indexed in Web of Science
• 97.6 million citation relations
• Classification system of 3 hierarchical levels:
– 20 broad disciplines
– 672 fields
– 22,412 subfields

19
Visualization of 672 research areas at
level 2 of the classification system

20
Visualization of 417 publications in
research area 4.30.10

21
Application in a science policy context

22
CitNetExplorer
(www.citnetexplorer.nl)

23
Exploring citation networks
• Macro-level applications:
– Studying the development of a research field over time
– Identifying research areas

• Micro-level applications:
– Studying the publication oeuvre of a researcher
– Supporting systematic literature reviewing

24
HistCite
• Timeline visualization of publications and their citation
relations, referred to as algorithmic historiography by
Eugene Garfield

25
CitNetExplorer
• New software tool for analyzing and visualizing citation
networks
• Freely available on www.citnetexplorer.nl
• Runs on any system that offers Java support
• Citation networks can be constructed directly based on
data downloaded from Web of Science
• Interactive functionality for drilling down into a citation
network
• Very large citation networks can be handled, with
millions of publications and tens of millions of citation
relations

26
Demonstration
• Database: Web of Science
• Fields: Physics and multidisciplinary (Nature, PLoS
ONE, PNAS, Science, etc.)
• Time period: 1998–2012
• Number of publications: ~1.8 million
• Number of citation relations: ~15.1 million

27
CitNetExplorer

28
References
Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for
bibliometric mapping. Scientometrics, 84(2), 523-538.
Van Eck, N.J., & Waltman, L. (2011). Text mining and visualization using VOSviewer. ISSI
Newsletter, 7(3), 50-54.
Van Eck, N.J., Waltman, L., Dekker, R., & Van den Berg, J. (2010). A comparison of two techniques for
bibliometric mapping: Multidimensional scaling and VOS. JASIST, 61(12), 2405-2416.
Waltman, L., & Van Eck, N.J. (2012). A new methodology for constructing a publication-level
classification system of science. JASIST, 63(12), 2378-2392.
Waltman, L., & Van Eck, N.J. (2013). A smart local moving algorithm for large-scale modularity-based
community detection. European Physical Journal B, 86(11), 471.
Waltman, L., Van Eck, N.J., & Noyons, E.C.M. (2010). A unified approach to mapping and clustering of
bibliometric networks. Journal of Informetrics, 4(4), 629-635.

29

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Applications of community detection in bibliometric network analysis

  • 1. Applications of community detection in bibliometric network analysis Nees Jan van Eck Centre for Science and Technology Studies (CWTS), Leiden University EURANDOM workshop “Networks with community structure”, Eindhoven January 24, 2014
  • 2. Outline • Bibliometric network analysis at CWTS • VOSviewer • Unified approach to visualization and community detection • Community detection in large citation networks • CitNetExplorer 1
  • 4. Bibliometric network analysis at CWTS • In-house databases: – Thomson Reuters Web of Science – Elsevier Scopus • Bibliometric networks: – Publication citation networks – Journal co-citation/bibliographic coupling networks – Term co-occurrence networks – Co-authorship networks – Etc. • Applications: – Research institutions: Research assessment – Scientific publishers: Journal profiling – Funding agencies: Science policy analyses 3
  • 6. VOSviewer (Van Eck & Waltman, Scientometrics, 2010) 5
  • 7. Citation network of fields in Web of Science 6
  • 8. Co-occurrence network of terms in clinical neurology 7
  • 10. Visualization vs. community detection • Visualization (‘mapping’): – Assigning the nodes in a network to locations in a (usually twodimensional) space • Community detection (‘clustering’): – Partitioning the nodes in a network into a number of groups 9
  • 11. Community detection seen as visualization in a restricted space 10
  • 12. Community detection seen as visualization in a restricted space 11
  • 13. Unified approach to visualization and community detection Minimize Q (x 1 , , x n ) i j 2m Aij d ij2 kik j d ij i j 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 Visualization Community detection xi: integer denoting the community to which node i belongs 0 if x i x j d ij 1 if x i x j : resolution parameter xi: vector denoting the location of node i in a p-dimensional space p d ij xi xj (x ik x jk )2 k 1 12
  • 14. Unified approach: Community detection Equivalent to a weighted variant of modularity-based community detection (Waltman et al., 2010) Maximize ˆ Q(x 1 , , x n ) 1 2m (x i , x j )w ij Aij i j kik j 2m where (xi, xj) equals 1 if xi = xj and 0 otherwise w ij 2m kik j 13
  • 15. Unified approach: Visualization • Equivalent to the VOS (visualization of similarities) technique (Van Eck & Waltman, 2007) • Limit case of multidimensional scaling (Van Eck et al., 2010) Q i j 2m Aij x i kik j Wij Dij xi xj 2 xi xj VOS i j xj 2 MDS i j Dij kik j 2m Aij 1 Wij 2m Aij kik j 14
  • 16. Unified approach Most commonly used community detection technique (modularity) and most commonly used visualization technique (MDS) can be brought together in a unified framework Unified approach Modularity (weighted) VOS MDS (limit case) 15
  • 18. Classification systems of scientific publications • Web of Science/Scopus classification systems: – Scientific fields defined at the level of journals rather than individual publications – Difficulties with multidisciplinary journals – High level of aggregation – Sometimes outdated or inaccurate • Disciplinary classification systems: – E.g., CA, JEL, MeSH, PACS – Not available for all disciplines – Sometimes outdated or inaccurate 17
  • 19. Algorithmically constructed classification systems • Publications (not journals) are clustered into fields based on citation relations • Fields are defined at different levels of granularity and are organized hierarchically • Community detection based on a variant of the standard modularity function that accounts for differences in citation practices across fields • Optimization using the smart local moving algorithm 18
  • 20. Example (Waltman & Van Eck, 2012) • 10.2 million publications from the period 2001–2010 indexed in Web of Science • 97.6 million citation relations • Classification system of 3 hierarchical levels: – 20 broad disciplines – 672 fields – 22,412 subfields 19
  • 21. Visualization of 672 research areas at level 2 of the classification system 20
  • 22. Visualization of 417 publications in research area 4.30.10 21
  • 23. Application in a science policy context 22
  • 25. Exploring citation networks • Macro-level applications: – Studying the development of a research field over time – Identifying research areas • Micro-level applications: – Studying the publication oeuvre of a researcher – Supporting systematic literature reviewing 24
  • 26. HistCite • Timeline visualization of publications and their citation relations, referred to as algorithmic historiography by Eugene Garfield 25
  • 27. CitNetExplorer • New software tool for analyzing and visualizing citation networks • Freely available on www.citnetexplorer.nl • Runs on any system that offers Java support • Citation networks can be constructed directly based on data downloaded from Web of Science • Interactive functionality for drilling down into a citation network • Very large citation networks can be handled, with millions of publications and tens of millions of citation relations 26
  • 28. Demonstration • Database: Web of Science • Fields: Physics and multidisciplinary (Nature, PLoS ONE, PNAS, Science, etc.) • Time period: 1998–2012 • Number of publications: ~1.8 million • Number of citation relations: ~15.1 million 27
  • 30. References Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. Van Eck, N.J., & Waltman, L. (2011). Text mining and visualization using VOSviewer. ISSI Newsletter, 7(3), 50-54. Van Eck, N.J., Waltman, L., Dekker, R., & Van den Berg, J. (2010). A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. JASIST, 61(12), 2405-2416. Waltman, L., & Van Eck, N.J. (2012). A new methodology for constructing a publication-level classification system of science. JASIST, 63(12), 2378-2392. Waltman, L., & Van Eck, N.J. (2013). A smart local moving algorithm for large-scale modularity-based community detection. European Physical Journal B, 86(11), 471. Waltman, L., Van Eck, N.J., & Noyons, E.C.M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629-635. 29