The document presents an analysis of European funded R&D collaborations, examining the structure and impact of project networks within the context of technology enhanced learning. It discusses key organizational collaborations, project relationships, and network parameters that indicate the influence of these projects on the collaboration landscape. Findings suggest a complex, scale-free network with significant correlations between funding, consortium size, and project impact.

1. 7th European Conference on
Technology Enhanced Learning (EC-TEL 2012)
September 18-21, 2012
Saarbrücken, Germany
The European TEL Projects
Community from a Social Network
Analysis Perspective
Michael Derntl and Ralf Klamma
RWTH Aachen University
Advanced Community Information Systems (ACIS)
Aachen, Germany
derntl@dbis.rwth-aachen.de
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
1 These slides are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

2. Motivation
Collaborative projects are key in the R&D value
chain
– Cost a lot of (tax payers’) money
– Drive research agenda and scientific community building
– scientific events (e.g. EC-TEL, summer school)
– researcher mobility, seed projects, R&D teams, associate
partnerships, etc.
– conducting, reporting, and disseminating research
– product development and knowledge transfer
Stakeholders have an interest in the collaboration
structures of their scientific community
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
– Key organizations, key projects, trends
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3. Research Objectives
Identify characteristics of the social network of funded
R&D collaborations
– Organizational collaboration
– Project relationships
– Central organizations and projects
Analyze impact of projects on the collaboration
landscape
– Conceive impact measure
– Find network parameters that may indicate impact
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Prof. Dr. M. Jarke
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4. Related Work
Several papers on collaboration networks in FP1-6 [2,
3, 4] with both one-mode and two-node networks
Community detection in collaboration networks [6, 7,
8], e.g. location, topics, org. type
Analysis of multimodal networks of NoEs (e.g. in
STELLAR) [1]
Findings
– complex scale-free networks; small diameter, high
clustering
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
– “oligarchic core” of organizations [5]
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5. Data set: Project timeline
9
# Started Projects
8
7
6
5
4
3
2
1
0
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
eTEN (39) – eLearning
FP6 (32) – TEL
eContentplus (19) – Educ.
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(Information Systems) FP7 (26) – TEL
Prof. Dr. M. Jarke
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6. Data set: Project timeline
116 projects
9 350
# Started Projects
EC Funding (Million Euro)
829 organizations
8
300
17progamme: 81% 250
26progammes: 14%
5 200
3 programmes: 4%
4 150
All programmes: 1% -- IMC, Open U,
3
WU Wien, KU Leuven, U Hannover, 100
U2Duisburg-Essen, Giunti 50
1
0 0
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
eTEN (39) – eLearning
FP6 (32) – TEL
eContentplus (19) – Educ.
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(Information Systems) FP7 (26) – TEL
Prof. Dr. M. Jarke
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7. Projects as Social Networks
Projects Organizations [4]
Project consortium progression
– Nodes: Projects ROLE
IMC, RWTH,
OU, ZSI
– Edges: Overlap of consortia TEL-Map
(directed, weighted)
Organizational collaboration
The Open STELLAR, EUROGENE,
– Nodes: Organizations University ROLE, PROLEARN,
iCOPER, ASPECT
– Edges: Collaboration in multiple KU
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(Information Systems)
Prof. Dr. M. Jarke
projects (undirected, weighted) Leuven
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8. Consortium Progression –
Project Network
Edge between project P1 and P2
– P2 started at least t time units after P1
– At least k overlapping partners in the consortia
– Edge direction: P1 P2
– Edge weight: function of overlap
Thresholds that filter for continued collaboration in
successive projects?
k=2
t = 3 months
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Prof. Dr. M. Jarke
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9. Consortium
Progression
k = 2, m = 3 months
Nodes 85
Edges 257
Diameter 4
Clustering coeff. 0.2
Avg. degree 6.05
Avg. weighted degree 16.9
Avg. path length 1.78
Node size proportional to
weighted degree
Node color represents
cluster [10]
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(Information Systems)
Prof. Dr. M. Jarke
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10. Consortium
Progression
k = 2, m = 3 months
Nodes 85
Edges 257
Diameter 4
Clustering coeff. 0.2
Avg. degree 6.05
Avg. weighted degree 16.9
Avg. path length 1.78
Node size proportional to
weighted degree
Node color represents
cluster [10]
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
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11. Project Impact on the Landscape
Measure impact of project consortium members on
sustaining and shaping the social TEL project ties after
the project start relative to opportunity.
,
∩
Impact ∙ Cumulative fraction
, of successor projects
∈ filled up with p's
Successor projects
relative to opportunity members
,
projects starting t time units after p and having at least k
partners overlap with p
all potential successor projects of p after t time units
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Prof. Dr. M. Jarke
consortium members of p
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12. Top 15 Projects by Impact
Filters
• Started at least 1y
before most recent
project batch (10/2010)
• Top 15
6 FP6, 3 FP7,
3 ECP, 2 eTEN
Top instruments:
6 STREP,
3 NoE,
2 BPN
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Prof. Dr. M. Jarke
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13. Correlations
Impact correlates positively with Pearson correlation *p < .1 **p < .05 ***p < .01
– Funding**, Consortium size***
– Betweenness centrality*** , (Weighted) in-degree** by size*
No correlation with PageRank, authority, hub, closeness
centrality, clustering coefficient
Promising (running or ended in last 12 months):
Funding m€ ▼wdin wdin/C
OpenDiscoverySpace 7.65 74 (26) 1.45
GALA 5.65 55 (20) 1.77
OpenScout 2.80 52 (17) 2.89
STELLAR 4.99 41 (14) 2.56
ROLE 6.60 35 (12) 2.19
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TEL-Map 2.13 31 (10) 3.10
(Information Systems)
Prof. Dr. M. Jarke iTEC 9.45 20 (5) 0.75
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14. Organizational Collaboration
Collaboration is the fertile soil for R&D output in CPs
Follow-up proposals / projects
Shapes the research agenda
Graph:
– Edge between O1 and O2 if both participated in at least
one project
The Open STELLAR, EUROGENE,
– Weight: number of projects University ROLE, PROLEARN,
iCOPER, ASPECT
– Direction: none KU
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Prof. Dr. M. Jarke – Nodes: organizations Leuven
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15. Organizational Collaboration
Network
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16. Organizational Collaboration
Network
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17. Collaboration Network Properties
Nodes 852 Clustering coefficient 0.89
Edges 12 021 Avg. degree 28.2
Avg. weighted degree 30
Diameter 6
Avg. path length 2.68
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18. Frequent Collaboration Pairs
National Centre For Wirtschaftsuniversitaet
4.0
Scientific Research Demokritos Wien 4.0
Giunti Labs S.R.L. 5.0
4.0
imc Information Multimedia Katholieke Universiteit
5.0 The Open University
5.0 Communication AG 5.0 6.0 Leuven
Ecole Polytechnique
4.0 Federale De Lausanne 4.0
4.0 5.0
Politecnico Di Milano
Jyvaskylan Yliopisto
Gottfried Wilhelm Leibniz Open Universiteit
4.0
Universitaet Hannover 6.0 Nederland
5.0
4.0 4.0
Deutsches Forschungszentrum Universitaet
4.0 Fuer Kuenstliche Intelligenz Gmbh Duisburg-Essen
4.0 4.0
Bundesministerium Fuer Atos Origin Sociedad Technische Universiteit Fraunhofer-Gesellschaft
Univerza V Ljubljani
Wissenschaft Und Forschung Anonima Espanola Eindhoven ... E.V
4.0 4.0 4.0
Ellinogermaniki Agogi
Universitaet Graz EUN Partnership Aisbl
Scholi Panagea Savva Ae
4.0 5.0 4.0
Bundesgymnasium Und The Provost Fellows
Tiigrihuppe Sihtasutus
Bundesrealgymnasium Schwechat ... Near Dublin
1. PROLEARN (FP6): 17 pairs 4. GRAPPLE (FP7): 8 pairs
2. ICOPER (ECP): 11 pairs 5. PROLIX (FP6): 6 pairs
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3. OpenScout (ECP): 9 pairs 6. STELLAR (FP7), ROLE (FP7): 5 pairs
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19. Projects Space @
LearningFrontiers.eu
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Prof. Dr. M. Jarke
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20. Projects Space @
LearningFrontiers.eu
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(Information Systems)
Prof. Dr. M. Jarke
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21. Projects Space @
LearningFrontiers.eu
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(Information Systems)
Prof. Dr. M. Jarke
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22. Dashboard
Lehrstuhl Informatik 5
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Prof. Dr. M. Jarke http://learningfrontiers.eu/?q=dashboard [9]
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23. Summary
Networks
– Collaboration: low diameter, high clustering
– Projects: low diameter, low clustering
– Small “oligarchic core” of frequent collaborators
– In line with previous research in FP1-6
Impact measure to account for time/size
– Correlates with in-degree, funding, betweenness centrality
– Networks (NoE and BPN) occupy 5 of top 8 spots
– Projects to follow: ODS, GALA, iTEC, ROLE, TEL-Map, …
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Explore data on learningfrontiers.eu

24. Limitations
Collaboration ties rest on people, not organizations
– EC deals with legal entities
– Partners deal with people
– People move on, legal entities merge and rebrand, etc.
Consortium overlaps may be random
Edges don’t fade over time, connections do
Data set
– Selection of programmes; LLP missing
– What is a “TEL related call”?
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– Missing associate memberships, seed projects, etc.

25. References
1. Voigt, C. (ed.): Deliverable D7.5, STELLAR Nework of Excellence (2011)
2. Barber, M., Krueger, A., Krueger, T., Roediger-Schluga, T.: Network of European Union–funded collaborative
research and development projects. Physical Review E 73 (2006)
3. Roediger-Schluga, T., Barber, M.J.: R&D collaboration networks in the European Framework Programmes: data
processing, network construction and selected results. International Journal of Foresight and Innovation Policy
4(3/4), 321–347 (2008)
4. Frachisse, D., Billand, P., Massard, N.: The Sixth Framework Program as an Affiliation Network: Representation
and Analysis (2008), http://ssrn.com/abstract=1117966
5. Breschi, S., Cusmano, L.: Unveiling the texture of a European Research Area: emergence of oligarchic networks
under EU Framework Programmes. International Journal of Technology Management 27(8), 747–772 (2004)
6. Lozano, S., Duch, J., Arenas, A.: Analysis of large social datasets by community detection. The European Physical
Journal Special Topics 143(1), 257–259 (2007)
7. Scherngell, T., Barber, M.J.: Spatial interaction modelling of cross-region R&D collaborations: empirical evidence
from the 5th EU framework programme. Papers in Regional Science 88(3), 531–546 (2009)
8. Roediger-Schluga, T., Dachs, B.: Does technology affect network structure? – A quantitative analysis of
collaborative research projects in two specific EU programmes. UNU-MERIT Working Paper Series 041 (2006)
9. Derntl, M., Erdtmann, S., Klamma, R.: An Embeddable Dashboard for Widget-Based Visual Analytics on Scientific
Communities. In: I-KNOW 2012. ACM (2012)
10. Blondel, V. D., Guillaume, J., Lambiotte, R., Lefevre, E.: Fast unfolding of communities in large networks. Journal of
Lehrstuhl Informatik 5 Statistical Mechanics: Theory and Experiment 2008 (10)
(Information Systems)
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