Download - The European TEL Projects Community from a Social Network Analysis Perspective

Lehrstuhl Informatik 5

(Information Systems)

Prof. Dr. M. Jarke

1 These slides are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

The European TEL Projects

Community from a Social Network

Analysis Perspective

Michael Derntl and Ralf Klamma

RWTH Aachen UniversityAdvanced Community Information Systems (ACIS)

Aachen, Germany

[email protected]

7th European Conference on

Technology Enhanced Learning (EC-TEL 2012)

September 18-21, 2012

Saarbrücken, Germany



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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

– Key organizations, key projects, trends



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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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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

– “oligarchic core” of organizations [5]



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1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

# Started Projects

Data set: Project timeline

eTEN (39) – eLearning

FP6 (32) – TEL

FP7 (26) – TEL

eContentplus (19) – Educ.



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1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

# Started Projects

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100

150

200

250

300

350

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

EC Funding (Million Euro)

Data set: Project timeline

eTEN (39) – eLearning

FP6 (32) – TEL

FP7 (26) – TEL

eContentplus (19) – Educ.

116 projects

829 organizations1 progamme: 81%

2 progammes: 14%

3 programmes: 4%

All programmes: 1% -- IMC, Open U, WU Wien, KU Leuven, U Hannover, U Duisburg-Essen, Giunti



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Projects as Social Networks

Projects � Organizations [4]

� Project consortium progression

– Nodes: Projects

– Edges: Overlap of consortia (directed, weighted)

� Organizational collaboration

– Nodes: Organizations

– Edges: Collaboration in multiple projects (undirected, weighted)

ROLE

TEL-Map

IMC, RWTH,

OU, ZSI

University

The Open

University

Leuven

KU

Leuven

STELLAR, EUROGENE,

ROLE, PROLEARN,

iCOPER, ASPECT



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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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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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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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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.

��,� 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

�� consortium members of p

Successor projects

relative to opportunity

Cumulative fraction

of successor projects

filled up with p's

members

� ��,��

∙ �� ∩ ��∈��,�

Impact



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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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Correlations

� Impact correlates positively with– 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

TEL-Map 2.13 31 (10) 3.10

iTEC 9.45 20 (5) 0.75

Pearson correlation *p < .1 **p < .05 ***p < .01



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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

– Weight: number of projects

– Direction: none

– Nodes: organizations

University

The Open

University

Leuven

KU

Leuven

STELLAR, EUROGENE,

ROLE, PROLEARN,

iCOPER, ASPECT



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Network



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Network



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Collaboration Network Properties

� Nodes 852

� Edges 12 021

� Diameter 6

� Clustering coefficient 0.89

� Avg. degree 28.2

� Avg. weighted degree 30

� Avg. path length 2.68



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Frequent Collaboration Pairs

1. PROLEARN (FP6): 17 pairs

2. ICOPER (ECP): 11 pairs

3. OpenScout (ECP): 9 pairs

4. GRAPPLE (FP7): 8 pairs

5. PROLIX (FP6): 6 pairs

6. STELLAR (FP7), ROLE (FP7): 5 pairs

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Open Universiteit Nederland

Gottfried Wilhelm Leibniz Universitaet Hannover

The Open UniversityKatholieke Universiteit

Leuvenimc Information Multimedia

Communication AG

Fraunhofer-Gesellschaft ... E.V

Jyvaskylan Yliopisto

Wirtschaftsuniversitaet Wien

National Centre For Scientific Research Demokritos

The Provost Fellows ... Near Dublin

Universitaet Graz

Deutsches Forschungszentrum Fuer Kuenstliche Intelligenz Gmbh

Atos Origin Sociedad Anonima Espanola

Universitaet Duisburg-Essen

Politecnico Di Milano

Giunti Labs S.R.L.

Ecole Polytechnique Federale De Lausanne

Technische Universiteit Eindhoven

EUN Partnership Aisbl

Univerza V Ljubljani

Tiigrihuppe SihtasutusBundesgymnasium Und

Bundesrealgymnasium Schwechat

Ellinogermaniki Agogi Scholi Panagea Savva Ae

Bundesministerium Fuer Wissenschaft Und Forschung



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Projects Space @

LearningFrontiers.eu



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Projects Space @




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Projects Space @




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Dashboard

http://learningfrontiers.eu/?q=dashboard [9]



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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, …

� Explore data on learningfrontiers.eu



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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”?

– Missing associate memberships, seed projects, etc.



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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

Statistical Mechanics: Theory and Experiment 2008 (10)

Download - The European TEL Projects Community from a Social Network Analysis Perspective

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