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A new software tool for large-scale
analysis of citation networks
Nees Jan van Eck
Centre for Science and Technology Studies (CWTS), Leiden University
Workshop “Measuring the Diversity of Research”, Berlin
September 2, 2013
Today’s talk
• Part 1: CWTS research program on bibliometric network
analysis
– VOSviewer
– VOS mapping and clustering
– Large-scale modularity optimization
– Algorithmically constructed publication-level classification system

• Part 2: New software tool for large-scale analysis of
citation networks

1
Part 1

CWTS research
program on
bibliometric
network analysis

2
VOSviewer (1)

(Van Eck & Waltman, Scientometrics, 2010)

3
VOSviewer (2)

(Van Eck & Waltman, Scientometrics, 2010)

4
Subject categories

5
Leiden University

6
Erasmus University Rotterdam

7
Delft University of Technology

8
Clinical Neurology

9
Clinical Neurology: Citation density

10

(Van Eck et al., PLoS ONE, 2012)
Clinical Neurology: Reference density

11
VOS mapping and clustering
• Mapping and clustering are commonly used bibliometric
network analysis techniques
• Mapping:
– Assigning the nodes in a network to locations in a (usually twodimensional) space
– VOS mapping technique has been developed specifically for mapping
bibliometric networks

• Clustering:
– Partitioning the nodes in a network into a number of groups (a.k.a.
community detection)
– VOS clustering technique has been developed to be used jointly with
the VOS mapping technique in a unified technical framework
12
Unified approach: Clustering seen as mapping
in a restricted space

13
Unified approach: Clustering seen as mapping
in a restricted space

14
Unified approach to mapping and
clustering
2mcij

Q( x1 ,, xn )

Minimize

i j

ci c j

2
dij

dij
i j

where
n: number of nodes in the network
m: number of links in the network

cij: number of links between nodes i and j
ci: number of links of node i

Mapping

Clustering

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

xi: integer denoting the cluster to
which node i belongs

p

dij

xi

xj

( xik
k 1

x jk )

2

dij

0

if xi

xj

1
if xi x j
: resolution parameter
15
Unified approach: Mapping
• Equivalent to the VOS mapping technique
• Closely related to multidimensional scaling (Van Eck et
al., JASIST, 2010)

16
Unified approach: Clustering
• Equivalent to a weighted and parameterized variant of
modularity-based clustering (Waltman et al., JOI, 2010)
ˆ
Q( x1 ,, xn )

Maximize

1
2m i

( xi , x j ) wij cij
j

ci c j
2m

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

wij

2m
ci c j

• Parameter makes it possible to customize the
granularity level of the clustering

17
Large-scale modularity optimization
• Modularity optimization is one of the most popular
approaches to clustering in networks
• Several variants of the original modularity function have
been proposed, supporting for instance weighted
networks and different resolution levels
• Optimization of modularity functions in large networks
(with millions of nodes and edges) has received only
limited attention but has important applications in
bibliometrics

18
New algorithm for large-scale modularity
optimization
• ‘Louvain algorithm’ (Blondel et al., 2008) is the bestknown algorithm for large-scale modularity optimization

• Our proposed ‘smart local moving algorithm’ can be
seen as an enhanced version of this algorithm (Waltman
& Van Eck, 2013)

19
Louvain algorithm
Q = 0.3791

Q = 0.4151

20
Smart local moving algorithm
Q = 0.3791

Q = 0.4198

21
Comparison
Network
Amazon
(0.5M / 0.9M)
DBLP
(0.4M / 1.0M)

IMDb
(0.4M / 15.0M)
LiveJournal
(4.0M / 34.7M)
WoS
(10.6M / 104.5M)
Web uk-2005
(39.5M / 783.0M)

Louvain

Smart local moving

Qmin

0.9257

0.9335

Qmax

0.9264

0.9338

6

28

Qmin

0.8203

0.8357

Qmax

0.8227

0.8367

7

26

Qmin

0.6976

0.7050

Qmax

0.7041

0.7077

18

100

Qmin

0.7441

0.7676

Qmax

0.7557

0.7720

350

1 549

Qmin

0.7714

0.7918

Qmax

0.7786

0.7957

6 800

19 994

Qmin

0.9793

0.9801

Qmax

0.9795

0.9801

t

11 006

17 074

t

t

t

t

t

22
Classification systems of scientific
publications
• Web of Science/Scopus journal subject categories:
– 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

23
Algorithmic classification systems
(Waltman & Van Eck, JASIST, 2012)
• Why not algorithmically construct a classification system
of science?
• We cluster publications (not journals) into fields based
on citation relations

• Only direct citation relations are used; no co-citation or
bibliographic coupling relations
• Fields are defined at different levels of granularity and
are organized hierarchically

24
Example
• 10.2 million publications from the period 2001–2010
indexed in Web of Science
• 97.6 million direct citation relations
• Classification system of 3 hierarchical levels:
– 20 broad disciplines
– 672 fields
– 22,412 subfields

• Clustering by optimizing a variant of the standard
modularity function that accounts for differences across
fields in citation practices
25
Map of the 672 research areas at level 2
of the classification system

26
Map of the 417 publications in research
area 4.30.10

27
Part 2

New software
tool for
exploring largescale citation
networks

28
Exploring citation networks: Why?
• To support literature reviewing
• To show how the scientific literature has evolved over
time
• To delineate topics or research areas in the literature
• To identify connections between different topics in the
literature

29
Motivation for a new tool
• VOSviewer has proven to be a very useful tool for
visualizing science from a static point of view
• VOSviewer has not been developed for visualizing the
dynamics of science

• In fact, the availability of software tools for dynamic
visualizations is rather limited:
– CiteSpace (Chaomei Chen)
– HistCite (Eugene Garfield)

30
HistCite
• Timeline visualization of publications and their citation
relations, referred to as algorithmic historiography by
Garfield

31
Citation Network Explorer
• Somewhat similar to HistCite, but capable of dealing with
much larger citation networks
• So far, the tool has been used successfully with the
entire Web of Science citation network of the social
sciences (1980–2013; ~2M publications and ~20M
citations)
• The aim is to be able to handle the entire citation
network of all scientific disciplines (~40M publications
and ~500M citations)

32
Today’s demonstration (1)
• We demonstrate a prototype of the tool
• The core functionality is available, but some options
have not yet been fully implemented
• Your feedback is very much appreciated!

33
Today’s demonstration (2)
• Data set 1:
– Scientometrics
– 1980–2013
– ~10K publications and ~60K citations

• Data set 2:
– All social sciences except for psychology, education, and health-related
sciences
– 1980–2013
– ~1.4M publications and ~10M citations

34
Citation Network Explorer

35
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.
Van Eck, N.J., Waltman, L., Van Raan, A.F.J., Klautz, R.J.M., & Peul, W.C. (2013). Citation analysis
may severely underestimate the impact of clinical research as compared to basic research.
PLoS ONE, 8(4), e62395.
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. arXiv:1308.6604.
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.

36

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A new software tool for large-scale analysis of citation networks

  • 1. A new software tool for large-scale analysis of citation networks Nees Jan van Eck Centre for Science and Technology Studies (CWTS), Leiden University Workshop “Measuring the Diversity of Research”, Berlin September 2, 2013
  • 2. Today’s talk • Part 1: CWTS research program on bibliometric network analysis – VOSviewer – VOS mapping and clustering – Large-scale modularity optimization – Algorithmically constructed publication-level classification system • Part 2: New software tool for large-scale analysis of citation networks 1
  • 3. Part 1 CWTS research program on bibliometric network analysis 2
  • 4. VOSviewer (1) (Van Eck & Waltman, Scientometrics, 2010) 3
  • 5. VOSviewer (2) (Van Eck & Waltman, Scientometrics, 2010) 4
  • 9. Delft University of Technology 8
  • 11. Clinical Neurology: Citation density 10 (Van Eck et al., PLoS ONE, 2012)
  • 13. VOS mapping and clustering • Mapping and clustering are commonly used bibliometric network analysis techniques • Mapping: – Assigning the nodes in a network to locations in a (usually twodimensional) space – VOS mapping technique has been developed specifically for mapping bibliometric networks • Clustering: – Partitioning the nodes in a network into a number of groups (a.k.a. community detection) – VOS clustering technique has been developed to be used jointly with the VOS mapping technique in a unified technical framework 12
  • 14. Unified approach: Clustering seen as mapping in a restricted space 13
  • 15. Unified approach: Clustering seen as mapping in a restricted space 14
  • 16. Unified approach to mapping and clustering 2mcij Q( x1 ,, xn ) Minimize i j ci c j 2 dij dij i j where n: number of nodes in the network m: number of links in the network cij: number of links between nodes i and j ci: number of links of node i Mapping Clustering xi: vector denoting the location of node i in a p-dimensional map xi: integer denoting the cluster to which node i belongs p dij xi xj ( xik k 1 x jk ) 2 dij 0 if xi xj 1 if xi x j : resolution parameter 15
  • 17. Unified approach: Mapping • Equivalent to the VOS mapping technique • Closely related to multidimensional scaling (Van Eck et al., JASIST, 2010) 16
  • 18. Unified approach: Clustering • Equivalent to a weighted and parameterized variant of modularity-based clustering (Waltman et al., JOI, 2010) ˆ Q( x1 ,, xn ) Maximize 1 2m i ( xi , x j ) wij cij j ci c j 2m where (xi, xj) equals 1 if xi = xj and 0 otherwise wij 2m ci c j • Parameter makes it possible to customize the granularity level of the clustering 17
  • 19. Large-scale modularity optimization • Modularity optimization is one of the most popular approaches to clustering in networks • Several variants of the original modularity function have been proposed, supporting for instance weighted networks and different resolution levels • Optimization of modularity functions in large networks (with millions of nodes and edges) has received only limited attention but has important applications in bibliometrics 18
  • 20. New algorithm for large-scale modularity optimization • ‘Louvain algorithm’ (Blondel et al., 2008) is the bestknown algorithm for large-scale modularity optimization • Our proposed ‘smart local moving algorithm’ can be seen as an enhanced version of this algorithm (Waltman & Van Eck, 2013) 19
  • 21. Louvain algorithm Q = 0.3791 Q = 0.4151 20
  • 22. Smart local moving algorithm Q = 0.3791 Q = 0.4198 21
  • 23. Comparison Network Amazon (0.5M / 0.9M) DBLP (0.4M / 1.0M) IMDb (0.4M / 15.0M) LiveJournal (4.0M / 34.7M) WoS (10.6M / 104.5M) Web uk-2005 (39.5M / 783.0M) Louvain Smart local moving Qmin 0.9257 0.9335 Qmax 0.9264 0.9338 6 28 Qmin 0.8203 0.8357 Qmax 0.8227 0.8367 7 26 Qmin 0.6976 0.7050 Qmax 0.7041 0.7077 18 100 Qmin 0.7441 0.7676 Qmax 0.7557 0.7720 350 1 549 Qmin 0.7714 0.7918 Qmax 0.7786 0.7957 6 800 19 994 Qmin 0.9793 0.9801 Qmax 0.9795 0.9801 t 11 006 17 074 t t t t t 22
  • 24. Classification systems of scientific publications • Web of Science/Scopus journal subject categories: – 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 23
  • 25. Algorithmic classification systems (Waltman & Van Eck, JASIST, 2012) • Why not algorithmically construct a classification system of science? • We cluster publications (not journals) into fields based on citation relations • Only direct citation relations are used; no co-citation or bibliographic coupling relations • Fields are defined at different levels of granularity and are organized hierarchically 24
  • 26. Example • 10.2 million publications from the period 2001–2010 indexed in Web of Science • 97.6 million direct citation relations • Classification system of 3 hierarchical levels: – 20 broad disciplines – 672 fields – 22,412 subfields • Clustering by optimizing a variant of the standard modularity function that accounts for differences across fields in citation practices 25
  • 27. Map of the 672 research areas at level 2 of the classification system 26
  • 28. Map of the 417 publications in research area 4.30.10 27
  • 29. Part 2 New software tool for exploring largescale citation networks 28
  • 30. Exploring citation networks: Why? • To support literature reviewing • To show how the scientific literature has evolved over time • To delineate topics or research areas in the literature • To identify connections between different topics in the literature 29
  • 31. Motivation for a new tool • VOSviewer has proven to be a very useful tool for visualizing science from a static point of view • VOSviewer has not been developed for visualizing the dynamics of science • In fact, the availability of software tools for dynamic visualizations is rather limited: – CiteSpace (Chaomei Chen) – HistCite (Eugene Garfield) 30
  • 32. HistCite • Timeline visualization of publications and their citation relations, referred to as algorithmic historiography by Garfield 31
  • 33. Citation Network Explorer • Somewhat similar to HistCite, but capable of dealing with much larger citation networks • So far, the tool has been used successfully with the entire Web of Science citation network of the social sciences (1980–2013; ~2M publications and ~20M citations) • The aim is to be able to handle the entire citation network of all scientific disciplines (~40M publications and ~500M citations) 32
  • 34. Today’s demonstration (1) • We demonstrate a prototype of the tool • The core functionality is available, but some options have not yet been fully implemented • Your feedback is very much appreciated! 33
  • 35. Today’s demonstration (2) • Data set 1: – Scientometrics – 1980–2013 – ~10K publications and ~60K citations • Data set 2: – All social sciences except for psychology, education, and health-related sciences – 1980–2013 – ~1.4M publications and ~10M citations 34
  • 37. 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. Van Eck, N.J., Waltman, L., Van Raan, A.F.J., Klautz, R.J.M., & Peul, W.C. (2013). Citation analysis may severely underestimate the impact of clinical research as compared to basic research. PLoS ONE, 8(4), e62395. 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. arXiv:1308.6604. 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. 36