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How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems
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How temporal network analysis can help us to explore existing interrelationships in online production systems

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  • 1. How temporal network analysis can help us to exploreexisting interrelationships in online production systemsDr. Claudia Müller-BirnInstitute for Computer Science, Group Networked Information SystemsJanuary 20, 2011Invited Talk, GESIS, Bonn
  • 2. When you think of the Social Web...How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 2Claudia Müller-Birn
  • 3. Social participation creates digital products STEM (Spatio-Temporal Exploratory Model) map Can Distributed Volunteers Accomplish of Dickcissel (http:// Massive Data Analysis Tasks? ebird.org) (Kanefsky et al., 2001) Graph of source lines of code added [millions] (Deshpande & Riehle, 2008) dataset based on www.ohloh.net Number of articles on English-language Wikipedia from its creation in 2001 through June 2010 (Riedl, 2011)How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 3Claudia Müller-Birn
  • 4. Social participation creates digital products • Geographically distributed communities STEM (Spatio-Temporal Exploratory Model) map Can Distributed Volunteers Accomplish • Very large number of granular, individual contributions Tasks? of Dickcissel (http:// Massive Data Analysis ebird.org) (Kanefsky et al., 2001) • Openness of boundaries, technical standards, communication and information sources • Peering as a new form of horizontal organization • Sharing of intellectual property Graph of source (Benkler, 2006), (OMahony, 2007), (Tapscott2007) lines of code added [millions] (Deshpande & Riehle, 2008) dataset based on www.ohloh.net Number of articles on English-language Wikipedia from its creation in 2001 through June 2010 (Riedl, 2011)How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 3Claudia Müller-Birn
  • 5. Outline• Dimensions in online production systems and existing research issues• Success in online production systems• Mirroring hypothesis in online production systems (research in progress)• Recent and future research challengesHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 4Claudia Müller-Birn
  • 6. Dimensions in online production systemspooled structured integralproduct product product How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 5 Claudia Müller-Birn
  • 7. Selected research issues in online production systemsMODELING QUALITY/SUCCESS• How do we model the • How do we measure quality or dimensions of online success? production systems? • How do online production• Which network systems strive for quality? descriptions are especially useful?• What are appropriate data sources?EVOLUTION• How do the social and the technical dimension INFLUENCE co-evolve? • How do we measure the• What techniques can be influence of the technical used for measuring and dimension on the social dimension describing evolution? and vice versa? • Are specific structures of networks more influential than others?How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 6Claudia Müller-Birn
  • 8. Success in online production systems: A longitudinal analysis of the socio-technical duality of development projects* Müller-Birn, C., Cataldo, M., Wagstrom, P., Herbsleb, J.D.: Success in Online Production Systems: A Longitudinal Analysis of the Socio-Technical Duality of Development Projects. Technical Report CMU-ISR-10-129, 2010.How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 7Claudia Müller-Birn
  • 9. What might be success factors for OPSs?Success of virtual community sites (Preece, 2000):Usability: human-technology interactions (e.g., information design, navigation, and access)Sociability: human-human interactions by developing policies and practices that are sociallyacceptable and practicable Success drivers are number of In Wikipedia the success of an article can participants who communicate, the be seen as its quality (Kittur & Kraut, number of exchanged messages, 2008) (there are certain requirements in interactivity, and reciprocity (Preece, order to get assigned into a six-level 2001) quality system, ranging from “stub” (almost no content) to “featured- article” (best quality))In product development,conceptualizations such as marketperformance of the product,project cycle time, efficiency of In open source projects typicallythe development process and quantifications of volume related to numberproduct quality are used (Clark & of contributors or participants orFujimoto, 1990), (Eisenhardt & number of access to the particularTabrizi, 1995), (Sethi, 2000) project’s product or outcome (Crowston et al., 2006), (Iriberri & Leroy, 2009) is usedHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 8Claudia Müller-Birn
  • 10. Open source software (OSS) project GNOME• Graphical user interface and a development framework for desktop applications• GNOME is a large collection of libraries and applications rather than a monolith application (German, 2003)• Data covered a period of about 8 years of activity from November 1997 until July 2005 Description Value Mail repository Number of emails 467,639 Number of senders 34,662 Date of first email 01-01-1997 Date of last email 02-10-2007 Code repository Number of committer 1,312 Number of commits 479,678 Number of files 286,314 Number of commits (files) 2,456,302 Date of first commit 12-22-1996 Date of last commit 08-01-2005 Bug repository Number of users 2,706 Number of bugs 201,068 Date of first bug 01-01-1999 Date of last bug 11-18-2005 How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 9 Claudia Müller-Birn
  • 11. How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 10Claudia Müller-Birn
  • 12. Used data set• Community hosted over 700 different projects• Projects differ significantly in their development activity, size, and participation rate• Projects were included if they satisfy all of the following criteria - Continuity of development activity (at least one year) - Amount of development activity (at least 100 commits) - Attractiveness of project for developers (at least 10 committers), - User interest to participate (at least one community hosted mailing list) - Data collected from different repositories should overlap during the analyzed period• Further used data set consists of 27 projectsHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 11Claudia Müller-Birn
  • 13. Social Dimension• Coordination needs network - Computation of coordination needs networks for each project by computing (Task Assignment ∗ Task Dependency) ∗ Transpose(Task Assignment) (Cataldo et al., 2008) - Task assignment: which individuals are working on which tasks - Task dependency: relationships or dependencies among tasks• Communication network - Construction of a collection of communication networks for each project from the project’s mailing list - Construction of communication networks of the whole OPS by aggregating the project-level communication networks into oneHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 12Claudia Müller-Birn
  • 14. Technical Dimension• Syntactic Dependency Network - Examination of source code and extracting data-related dependency (e.g., a particular data structure modified by a function and used in another function) and functional dependency (e.g., method A calls method B) relationships between source code files during the period of time between two releases of the GNOME distribution• Logical Dependency Network - Construction of the logical dependencies network by extracting the set of source code files that were modified as part of development tasks performed during the period of time between two releases of the GNOME distributionHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 13Claudia Müller-Birn
  • 15. Results• Successful projects benefit from interaction patterns that are able to disseminate information to most of the project participants while minimizing redundant interconnections• Successful projects exhibit a continuously active core group that is able to integrate all member of the project or the developed software• Project success depends on its members occupying different structural positions within the network as a mechanism to balance the benefits and limitations of belonging solely to the core or the periphery• When tasks dependencies are partitioned among separate clusters of highly interdependent sets of individuals, projects are more likely to succeed• Modular technical structures (those with independent clusters of highly interdependent parts) are an important success driver for online production systemsHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 14Claudia Müller-Birn
  • 16. Mirroring hypothesis in online production systems using temporal network analysis (research in progress)How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 15Claudia Müller-Birn
  • 17. Co-evolution of social and technical architectures• Social architecture should reflect the technical architecture of a system and vice versa in order to improve the degree of innovation or to reduce the coordination needs (Conway, 1968), (Baldwin & Clark, 2000), (Cataldo et al., 2008)• Open collaborative communities are geographically distributed; therefore, their technical architecture should be modular (e.g., (Moon & Sproull, 2000))• In the context of OSS, a modular technical architecture increases incentives to join and decreases free riding (Baldwin & Clark, 2006), (West & Mahony, 2008)• BUT recent empirical work has shown that this hypothesis can only be partly supported in open collaborative settings (Colfer & Baldwin 2010)How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 16Claudia Müller-Birn
  • 18. Requirements for model description• Networks are used to describe communities therefore the relation between the people (density of links) should be used as measure• Evolution of networks over time; therefore, a temporal model is required• Large membership base in open collaborative communities therefore the algorithm should be able to deal with large networks• Complete knowledge about the networks is often not available therefore the algorithm should detect local communities• People are often actively involved in different communities; therefore, the algorithm should allow overlapping communitiesHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 17Claudia Müller-Birn
  • 19. Brief overview on existing approaches• Discrete approach to consider time in graphs (Moody, 2005) - Cross-sectional analysis of graphs where the main focus lies on the changes of network stages (e.g., (Cortes, 2003), (Sun, 2007)) - Approaches to discretize the interactions (a) the cumulative approach and (b) the time window approach• Continuous approach to consider time in graphs (Moody, 2005) - Each single interaction with a start and end date is considered (e.g., (Kumar, 2003r), (Priebe, 2005))• Describing evolution in networks based on a group-level - Network quality function (Mucha et al. 2010) - Dynamic tensor analysis (Sun et al. 2006) - Evolutionary spectral clustering (Chi et al. 2007) - Clique percolation method (Palla et al., 2005)How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 18Claudia Müller-Birn
  • 20. Experimental setup• Data set: OOS project Epiphany (web browser)• Communication network based on mailing list repository• One time frame considers three months of activity• Steps of CPM Description Value - Locate all complete subgraphs, i.e. cliques, # month 44 that are not part of a larger subgraph # senders 688 # mails 8,352 - Identify communities based on # threads 1,294 clique-clique overlap matrix # committers 208 - Specify “optimal” percolation structure # commits 5,898 # files 21,223 # added LOC 957,091 # removed LOC 748,956 mails per person 12.00 persons per thread 6.45 commits per person 28.36 How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 19 Claudia Müller-Birn
  • 21. Selected community and network characteristics 10,000 0.003 9 edges 8 nodes 0.0025 1,000 7!"#$%&()!(*%+,%*-%+ 0.002 6 !"#$%&%( 5 !"#$%&( 100 0.0015 4 0.001 3 10 2 0.0005 1 1 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 $#)*$+,&( +!./+0(1 )*#+),-&( 90.00% 90 k=3 not included k=3 80.00% 80 k=4 not included k=4 k=5 not included percentage of non-included nodes 70.00% 70 k=5 !"#$%&%()*+$!,%-&../0",1% 60.00% 60 50.00% 50 40.00% 40 30.00% 30 20.00% 20 10.00% 10 0.00% 0 !" #" $" %" &" " (" )" *" !+" 1 2 3 4 5 6 7 8 9 10 snapshot !0)2!3&,% How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 20 Claudia Müller-Birn
  • 22. Community development based on social interactions 90 new (leaving) 80 new old (leaving) 70 old 60 50size 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 snapshot How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 21 Claudia Müller-Birn
  • 23. Recent and future research challengesHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 22Claudia Müller-Birn
  • 24. Conclusions• Considering time by describing the two dimensions helps to reveal dependencies between development patterns and the specific life cycle stage of an OPS• Success of an online production system is related to the social AND technical dimension; thus, describing both dimensions is a requirement to understand and to improve existing production processes• Other research has shown that organizational and technical structures are related; necessity to explore existing interdependencies in OPSsHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 23Claudia Müller-Birn
  • 25. Thank you. Acknowledgements Co-authors: Marcelo Cataldo, James D. HerbslebHow temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 24Claudia Müller-Birn
  • 26. References• C.Y. Baldwin and K.B. Clark: Design Rules: The Power of Modularity Volume 1. MIT Press, Cambridge, MA, USA, 1999.• C.Y. Baldwin and K.B. Clark. The Architecture of Participation: Does Code Architecture Mitigate Free Riding in the Open Source Development Model? Management Science. 52:7. 2006.• Benkler, Y., & Nissenbaum, H. Commons based Peer Production and Virtue*. Journal of Political Philosophy, 14(4): 394-419. 2006.• M. Cataldo, J.D. Herbsleb, K.M. Carley. Socio-technical congruence: a framework for assessing the impact of technical and work dependencies on software development productivity, Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement: 2-11. Kaiserslautern, Germany: ACM. 2008.• Y. Chi, S. Zhu, X. Song, J. Tatemura and B.L. Tseng. Structural and temporal analysis of the blogosphere through community factorization. KDD 07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. San Jose, California, USA, 163-172. 2007.• K. Clark and T. Fujimoto. Product Development Performance. Harvard Business School Press, 1991.• M. E. Conway. How do Committees Invent? Datamation. 14:4. 28-31. 1968.• C. Cortes, D. Pregibon and C. Volinsky: Computational Methods for Dynamic Graphs. Journal of Computational and Graphical Statistics. 12:4. 950-970. 2003.• K. Crowston, J. Howison, H. Annabi, H. Information systems success in free and open source software development: theory and measures. Software Process: Improvement and Practice, 11(2): 123-148. 2006.• A. Deshpande and D. Riehle: The Total Growth of Open Source. Proceedings of the Fourth Conference on Open Source Systems (OSS 2008). Springer Verlag. 197-209. 2008.• K. Eisenhardt and B. Tabrizi. Accelerating adaptive processes: Product innovation in the global industry. Administrative Science Quarterly, 40(1):84–110, 1995.• A. Iriberri and G. Leroy. A life-cycle perspective on online community success. ACM Comput. Surv., 41(2):1–29, 2009.• A. Kittur and R. E. Kraut. Harnessing the wisdom of crowds in wikipedia: quality through coordination. In Proc. of CSCW, pages 37–46, 2008.• B. Kanefsky, N.G. Barlow, V.C. Gulick. Can Distributed Volunteers Accomplish Massive Data Analysis Tasks?. 32nd Annual Lunar and Planetary Science Conference. 2001.• R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. WWW 03: Proceedings of the 12th international conference on World Wide Web. ACM, New York, NY, USA. 568--576. 2003.How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 25Claudia Müller-Birn
  • 27. References (cont.)• J. Moody, D. McFarland and S. Bender-deMoll. Dynamic Network Visualization. American Journal of Sociology. 110:4. 1206-1241. 2005.• J.Y. Moon and L. Sproull. Essence of Distributed Work: The Case of the Linux Kernel. First Monday. 5:11. 2000.• L. Sproull and S. Kiesler. Connections - new ways of working in the networked organization. MIT Press. Cambridge, Mass. 1995.• P.J. Mucha, T. Richardson, K. Macon, M.A. Porter, J-P. Onnela: Community Structure in Time-Dependent, Multiscale, and Multiplex Networks. Science. 328: 5980. 876-878. 2010.• C. Müller-Birn, M. Cataldo, P. Wagstrom, J.D. Herbsleb: Success in Online Production Systems: A Longitudinal Analysis of the Socio-Technical Duality of Development Projects. Technical Report CMU-ISR-10-129, 2010.• OMahoney, S., & Ferraro, F. The emergence of governance in an open source community. Academy of Management Journal, 50(5): 1079-1106. 2007.• G. Palla, I. Dereny, I. Farkas, I, T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature. 435: 7043. 814-818. 2005.• J. Preece. Online Communities: Designing Usability, Supporting Sociability. John Wiley & Son, 2000.• J. Preece. Sociability and usability in online communities: determining and measuring success. Behav. & Inform. Techn., 20 (5):347–356, 2001.• C.E. Priebe, J.M. Conroy, D.J. Marchette, and Y. Park. Scan Statistics on Enron Graphs. Computational and Mathematical Organization Theory Journal. 11:3. 229-247. 2005• J. Riedl. The Promise and Peril of Social Computing. Computer. 44:1. 93-95. 2011.• R. Sethi. New product quality and product development teams. Journal of Marketing, 64:1–14, 2000.• J. Sun, D. Tao and C. Faloutsos. Beyond streams and graphs: dynamic tensor analysis. KDD 06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. New York, NY, USA. 374-383. 2006.• J. Sun. Incremental pattern discovery on streams, graphs and tensors (phdthesis). CMU. Pittsburgh, PA, USA. 2007.• D. Tapscott, A. Williams. Wikinomics: How mass collaboration changes everything: Portfolio Trade. 2008.• J. West and S. OMahony. The Role of Participation Architecture in Growing Sponsored Open Source Communities. Industry and Innovation. 15:2. 145-168. 2008.How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 26Claudia Müller-Birn

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