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Network visualization: Fine-tuning
layout techniques for different types of
networks
Nees Jan van Eck and Ludo Waltman
Cen...
VOSviewer
1
Example
2
Layout problem
• How to position the nodes of a network in a 2D
space in an attractive way?
• What do we mean by ‘attracti...
VOS (visualization of similarities)
layout technique
• Quality function to be minimized:
xi: Location of node i in 2D spac...
Co-authorship network
5
α = 2
α = 3
α = 4
α - β = 5 α - β = 4 α - β = 3 α - β = 2 α - β = 1
Co-authorship network
(attraction = 2, repulsion = 1)
6
Co-authorship network
(attraction = 2, repulsion = 0)
7
Co-authorship network
(attraction = 2, repulsion = -1)
8
Co-authorship network
(attraction = 2, repulsion = -2)
9
Citation network of journals
10
α = 1
α = 2
α = 3
α - β = 4 α - β = 3 α - β = 2 α - β = 1
Citation network of journals
(attraction = 2, repulsion = 1)
11
Citation network of journals
(attraction = 2, repulsion = 1)
12
Citation network of journals
(attraction = 1, repulsion = 0)
13
Citation network of journals
(attraction = 1, repulsion = 0)
14
Citation network of journals
(attraction = 1, repulsion = 0)
15
Systematic layout comparison using a
meta criterion
• Meta criterion of Chen and Buja (2009) can be used
to set the attrac...
Network data
• Bibliometric networks:
– Co-authorship networks
– Citation networks
– Co-citation networks
– Bibliographic ...
Optimal attraction and repulsion
values according to meta criterion
18
Network Attraction Repulsion
Author bib. coup. 1 0
...
Conclusions
• Attraction = 2 and repulsion = 1 (default values)
usually work reasonably well both for static and for
inter...
Thank you for your attention!
20
References
Chen, L.S., & Buja, A. (2009). Local multidimensional scaling for
nonlinear dimension reduction, graph drawing,...
Network statistics
22
Network
No.
nodes
No.
edges
Density
Avg.
degree
St. dev.
degree
Radius Diameter
Avg.
path
length
Avg...
Network statistics
23
Network
No.
nodes
No.
edges
Density
Avg.
degree
St. dev.
degree
Radius Diameter
Avg.
path
length
Avg...
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Network visualization: Fine-tuning layout techniques for different types of networks

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An important issue in network visualization is the problem of obtaining a good layout for a network. For a given network, which may be either weighted or unweighted, the problem is to position the nodes in the network in a two-dimensional space in such a way that an attractive layout is obtained. Many layout techniques have been proposed [1]. In the visualization of bibliometric networks, multidimensional scaling and the layout technique of Kamada and Kawai [2] have for instance been used a lot. More recently, the VOS (visualization of similarities) layout technique [3], implemented in our VOSviewer software (www.vosviewer.com) [4], is often used for bibliometric network visualization.

There is no layout technique that is generally considered to give optimal results. One reason for this is that comparisons between layouts produced by different techniques involve a lot of subjectiveness. Someone may consider one layout to be more attractive than another, but someone else may have an opposite opinion on this. In addition, the attractiveness of a layout may depend on the type of visualization that is needed. For instance, some layouts may be more attractive for interactive visualizations (e.g., in a software tool with zooming functionality), while other layouts may be more attractive for static visualizations. Furthermore, different types of networks may benefit from different layout techniques.

In recent studies [5, 6], the idea of parameterized layout techniques has been introduced. Parameterized layout techniques produce different types of layouts depending on the values chosen for their parameters. In this research, we present a comprehensive study of a parameterized version of our VOS layout technique. Two parameters are included. Like in [5], these are referred to as attraction and repulsion parameters. We compare the layouts obtained for different parameter values. Comparisons are made both subjectively using the VOSviewer software (i.e., which layout do we find most appealing?) and more objectively using so-called meta-criteria [6, 7]. Sensitivity to local optima is taken into account as well. Comparisons are made for all important types of bibliometric networks, in particular co-authorship, citation, co-citation, bibliographic coupling, and co-occurrence networks. Both smaller and larger networks are considered.

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Network visualization: Fine-tuning layout techniques for different types of networks

  1. 1. Network visualization: Fine-tuning layout techniques for different types of networks Nees Jan van Eck and Ludo Waltman Centre for Science and Technology Studies (CWTS), Leiden University Fifth International Workshop on Social Network Analysis (ARS'15) Capri, Italy, April 30, 2015
  2. 2. VOSviewer 1
  3. 3. Example 2
  4. 4. Layout problem • How to position the nodes of a network in a 2D space in an attractive way? • What do we mean by ‘attractive’? – Related nodes are located close to each other – Groups of related nodes are clustered together – Sufficient empty space between nodes; no overlapping nodes – ... • Attractiveness may depend on: – Type of visualization (static vs. interactive) – Type of network (small vs. large; sparse vs. dense) 3
  5. 5. VOS (visualization of similarities) layout technique • Quality function to be minimized: xi: Location of node i in 2D space aij: Weight of edge between nodes i and j α and β: Attraction and repulsion parameters (α > β) • Traditional VOS layout technique is obtained by setting α = 2 and β = 1 • Technique similar to LinLog (Noack, 2009) is obtained by setting α = 1 and β = 0 4    ji β ji ji α jiijn β a α Q xxxxxx 11 ),,( 1 
  6. 6. Co-authorship network 5 α = 2 α = 3 α = 4 α - β = 5 α - β = 4 α - β = 3 α - β = 2 α - β = 1
  7. 7. Co-authorship network (attraction = 2, repulsion = 1) 6
  8. 8. Co-authorship network (attraction = 2, repulsion = 0) 7
  9. 9. Co-authorship network (attraction = 2, repulsion = -1) 8
  10. 10. Co-authorship network (attraction = 2, repulsion = -2) 9
  11. 11. Citation network of journals 10 α = 1 α = 2 α = 3 α - β = 4 α - β = 3 α - β = 2 α - β = 1
  12. 12. Citation network of journals (attraction = 2, repulsion = 1) 11
  13. 13. Citation network of journals (attraction = 2, repulsion = 1) 12
  14. 14. Citation network of journals (attraction = 1, repulsion = 0) 13
  15. 15. Citation network of journals (attraction = 1, repulsion = 0) 14
  16. 16. Citation network of journals (attraction = 1, repulsion = 0) 15
  17. 17. Systematic layout comparison using a meta criterion • Meta criterion of Chen and Buja (2009) can be used to set the attraction and repulsion parameters: 1. For each node, select the k most strongly related nodes 2. For each node, select the k nearest neighbors in the 2D space 3. Calculate the overlap of the two sets of nodes 4. Meta criterion equals the sum of the overlap over all nodes • We set k = 25 16
  18. 18. Network data • Bibliometric networks: – Co-authorship networks – Citation networks – Co-citation networks – Bibliographic coupling networks – Co-occurrence networks • Other networks: – Zachary's karate club – Les Miserables – American College football – Dolphin social network – US political books – Power grid 17
  19. 19. Optimal attraction and repulsion values according to meta criterion 18 Network Attraction Repulsion Author bib. coup. 1 0 Author cocitation 1 0 Journal citation 1 0 Journal cocitation 1 1 0 Journal cocitation 2 1 0 Term cooccurrence 1 0 Univ. coauthorship 1 0 Publication citation 1 -1 Author coauthorship 1 -3 Network Attraction Repulsion Football 1 0 Dolphins 1 -1 Les Miserables 1 -1 Political books 1 -1 Power grid 1 -1 Karate club 1 -4
  20. 20. Conclusions • Attraction = 2 and repulsion = 1 (default values) usually work reasonably well both for static and for interactive visualization • Attraction = 1 and repulsion = 0 (LinLog) often yield best layout for interactive visualization • Very sparse networks (e.g., co-authorship) may benefit from a negative repulsion • Low repulsion leads to more uniform and less clustered layouts, which may be attractive for static visualization 19
  21. 21. Thank you for your attention! 20
  22. 22. References Chen, L.S., & Buja, A. (2009). Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis. Journal of the American Statistical Association, 104(485), 209–219. http://dx.doi.org/10.1198/jasa.2009.0111 Noack, A. (2009). Modularity clustering is force-directed layout. Physical Review E, 79(2), 026102. http://dx.doi.org/10.1103/PhysRevE.79.026102 Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. http://dx.doi.org/10.1007/s11192-009-0146-3 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. http://dx.doi.org/10.1002/asi.21421 21
  23. 23. Network statistics 22 Network No. nodes No. edges Density Avg. degree St. dev. degree Radius Diameter Avg. path length Avg. clustering coefficient Global clustering coefficient Author bib. coup. 174 11739 0.780 134.93 34.38 2 3 1.22 0.89 0.72 Author coauthorship 242 562 0.019 4.64 4.07 6 12 4.87 0.56 0.17 Author cocitation 552 49090 0.323 177.86 86.24 2 3 1.68 0.58 0.27 Journal citation 5000 1155096 0.092 462.04 352.36 2 4 1.94 0.42 0.16 Journal cocitation 1 420 38188 0.434 181.85 73.20 1 2 1.57 0.64 0.31 Journal cocitation 2 232 4112 0.153 35.45 20.41 2 4 1.97 0.49 0.18 Pub. citation 1955 5636 0.003 5.77 6.22 10 18 5.59 0.13 0.05 Term cooccurrence 597 51186 0.288 171.48 92.41 2 2 1.71 0.53 0.22 Univ. coauthorship 500 103870 0.833 415.48 64.83 1 2 1.17 0.88 0.69
  24. 24. Network statistics 23 Network No. nodes No. edges Density Avg. degree St. dev. degree Radius Diameter Avg. path length Avg. clustering coefficient Global clustering coefficient Karate club 34 78 0.139 4.59 3.88 3 5 2.41 0.57 0.10 Les Miserables 77 254 0.087 6.60 6.04 3 5 2.64 0.57 0.25 Football 115 613 0.094 10.66 0.89 3 4 2.51 0.40 0.19 Dolphins 62 159 0.084 5.13 2.96 5 8 3.36 0.26 0.13 Political books 105 441 0.081 8.40 5.47 4 7 3.08 0.49 0.15 Power grid 4941 6594 0.001 2.67 1.79 23 46 18.99 0.08 0.04

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