This document discusses using two-mode networks to analyze collaboration in digital ecosystems. It compares using one-mode vs two-mode networks, finding two-mode networks allow a more detailed analysis while avoiding information loss. The document applies this approach to wiki data, identifying collaboration patterns like "pioneers" and "community stars". It concludes two-mode networks can better define, identify, and visualize patterns of collaboration compared to one-mode networks.

1. Visualization of Networked Collaboration in Digital
Ecosystems through Two-mode Network Patterns
International Conference on Management of Emergent Digital
EcoSystems (MEDES’11), San Francisco, November 21-23, 2011
Felix Mödritscher, Wolfgang Taferner
Vienna University of Economics and Business, Austria
Ahmet Soylu, Patrick De Causmaecker
Department of Computer Science, K.U. Leuven, Belgium
© role-project.eu

2. Overview
1. Motivation and conceptual approach
Application of two-mode networks for analyzing collaboration in Digital Ecosystems
2. Related work
Visualization techniques, network modes, & pattern mining in networked structures
3. Comparison of different network visualizations
Methodology, network modes, pattern detection, & homogeneity considerations
4. Conclusions & future work
Pros and cons of this research; next steps
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3. 1. Motivation and research design
 Learners and knowledge workers are
part of a Digital Ecosystem
 Complex interaction flows with other
actors, tools, artifacts, & communities
 Many analysis methods based on
one-mode networks (like SNA), thus
reducing complexity but also information
Research design:
 Idea: Application of two-mode networks to analyze and visualize
networked collaboration in Digital Ecosystems
 (a) review of related work, (b) comparing one and two-mode networks
21-23/11/2011 MEDES’11, San Francisco, 2011 3/11 , © role-project.eu

4. 2. Related work: Visualization techniques & network modes
Visualization techniques
 linear structures, hierarchies, networks, multi-dim. spaces, maps [Andrews 2002]
 exploration environments for discovering relationships, data & document mining,
analyzing mass data-sets, creating visual awareness etc. [Fayyad et al. 2002]
 Web 2.0: harnessing the collective intelligence of Digital Ecosystems (Facebook,
Google, Bing, Apple Stores, Mendeley, Flickr etc.)
Network modes
 network visualization can “help users to
understand and manage the complexity of
large, structured hypermedia collections”
[Andrews 1998]
 however, one-mode networks in most cases
(SNA, tag networks, citation networks etc.)
 two-mode networks: bipartite graphs
 e.g. ‘Deep South’ project [Davis et al. 1941]
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5. 2. Related work: Pattern mining in networked structures
Network pattern: defined according to relations between nodes as well as
thresholds for metrics on nodes and edges (rules)
PALADIN [Klamma et al. 2006]: patterns in
social networks (roles like conversionalists
or trolls)
 restricted to one-mode networks
gSpan [Yan & Han 2002]: performant
technique for graph-based substructure
pattern mining
 not applicable for bipartite graphs
Blockmodeling approach [Dorejan et al. 2004]:
identifying patterns in k-partite graphs
 very costly processing (not performant)
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6. 3. Comparison of visualizations: Methodology
 Development of own algorithms to pre-process data, generate two-
mode networks and visualize collaboration in Digital Ecosystems
 Focus on MediaWiki instances
 Wiki of the SONIVIS project (19 contributors, 342 articles, 600 relations)
 German version of Wikiversity (1.695 contributors, 9.751 articles, 21.288
relations)
 German version of Wikiquote (25.911 contributors, 9.120 articles, 136.383
relations)
 Raw data from MySQL database, transformed according to a
specific data model (used by the SONIVIS software)
 Imported into the R framework, an open source software for
statistical computing and graphics
 Creation of two-mode networks and visualizations with R
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7. 3. Comparison of visualizations: One vs. Two-mode Networks
One-mode networks:
+ better overview
+ only one type of nodes (easier to understand)
- loss of information (women connected by
which event(s)?)
Two-mode networks:
+ can be more complex and realistic for a
real-world problem
+ possibility of more detailed analysis
- might be too much details (information
overload)
Conclusion: combined application of both
network types (overview, details on demand)
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8. 3. Comparison of visualizations: Pattern detection
 Roles identifiably in one and two-mode networks!
 Yet, two-mode networks allow defining rules
in a more fine-grained way (on bipartite graphs)
 Pioneers within the Wikiversity platform
(Pioneer = at least 0.1% of all articles AND 40%
to 80% ratio of single to multi-authored articles)
 Furthermore, two-mode networks enable
zooming into large networks through
neighbourhood visualizations of patterns:
SONIVIS vs. Wikiversity
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9. 3. Comparison of visualizations: Homogeneity of networks
 Collaboration can be characterized in terms of the homogeneity of a
networks  Wikiversity vs. Wikiquote
 Wikiversity: slightly connected sub-networks with 11 ‘Pioneers’, 4
‘Networkers’ (connected to a wide range of users) and 1 ‘Community
Star’ (well connected through their contributions)
 Wikiquote: relations between articles and contributes homogeneously
distributed; 0 ‘Pioneers’, 19 ‘Networkers’ and 25 ‘Community Stars’
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10. 4. Conclusions and future work
Discussion of our pattern approach for two-mode networks:
 More detailed analysis and semantics for real-world problems (no
loss of information)
 Possibility to define, identify and visualize patterns of
collaboration in Digital Ecosystems (like Wikis)
 Possibility to zoom into large networks (avoiding information
overload)
 Depending on scenario, one-mode network visualization might be
better (to reduce complexity or the network size)
Future work:
 Application of the pattern-based analysis and visualization
approach to other domains (like personal learning environments)
 Further exploration studies with other Wikis
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11. Thanks for your attention!
The research leading to these results has received funding from the European
Community's Seventh Framework Programme (FP7/ 2007-2013) under grant
agreement no 231396 (ROLE project).
21-23/11/2011 MEDES’11, San Francisco, 2011 11/11, © role-project.eu