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

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Outline

• Bibliometric network analysis at CWTS

• VOSviewer

• Unified approach to visualization and community detection

• Community detection in large citation networks

• CitNetExplorer

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Bibliometric network analysisat CWTS

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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

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VOSviewer(www.vosviewer.com)

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VOSviewer

(Van Eck & Waltman, Scientometrics, 2010)

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Citation network of fields in Web of Science

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Co-occurrence network of terms in clinical neurology

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Unified approach to visualization andcommunity detection

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Visualization vs. community detection• Visualization (‘mapping’):

– Assigning the nodes in a network to locations in a (usually two-dimensional) space

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

1111

Community detection seen as visualization in a restricted space

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Community detection seen as visualization in a restricted space

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Unified approach to visualization and community detection

Minimize

wheren: 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

ji

ijji

ijijji

n ddAkkm

xxQ 21

2),,(

Visualizationxi: vector denoting the

location of node i in a p-dimensional space

p

kjkikjiij xxxxd

1

2)(

Community detectionxi: integer denoting the

community to which node i belongs

: resolution parameter

ji

jiij xx

xxd

if 1

if 0

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Unified approach: Community detection

Equivalent to a weighted variant of modularity-based community detection (Waltman et al., 2010)

Maximize

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

ji

jiijijjin m

kkAwxx

mxxQ

2),(

21

),,(ˆ1

jiij kk

mw

2

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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)

ji

jiji

jiijji

xxxxAkkm

Q22

ji

jiijij xxDW2

1

2 ij

jiij A

m

kkD ij

jiij A

kkm

W2

VOS

MDS

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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)

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Community detection in large citation networks

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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

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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

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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

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

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Visualization of 417 publications in research area 4.30.10

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Application in a science policy context

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CitNetExplorer(www.citnetexplorer.nl)

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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

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HistCite

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

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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

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

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CitNetExplorer

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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.