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The European TEL Projects Community from a Social Network Analysis Perspective
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
Lehrstuhl Informatik 5
(Information Systems)
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.
Lehrstuhl Informatik 5
(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.
Lehrstuhl Informatik 5
(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
Lehrstuhl Informatik 5
(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
Lehrstuhl Informatik 5
(Information Systems)
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]
Lehrstuhl Informatik 5
(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
Lehrstuhl Informatik 5
(Information Systems)
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
Lehrstuhl Informatik 5
(Information Systems)
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
Lehrstuhl Informatik 5
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
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke – Nodes: organizations Leuven
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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, …
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
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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”?
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
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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)
Prof. Dr. M. Jarke
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