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Research Papers

Analysis of Collaborative Design Networks: A Case Study of OpenIDEO

[+] Author and Article Information
Mark Fuge

Department of Mechanical Engineering,
Berkeley Institute of Design,
University of California,
Berkeley, CA 94709
e-mail: mark.fuge@berkeley.edu

Kevin Tee

Department of Computer Science,
Berkeley Institute of Design,
University of California,
Berkeley, CA 94709
e-mail: kevintee@berkeley.edu

Alice Agogino

Department of Mechanical Engineering,
Berkeley Institute of Design,
University of California,
Berkeley, CA 94709
e-mail: agogino@berkeley.edu

Nathan Maton

IDEO,
San Francisco, CA 94105
e-mail: nmaton@ideo.com

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received December 17, 2013; final manuscript received January 9, 2014; published online March 12, 2014. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 14(2), 021009 (Mar 12, 2014) (8 pages) Paper No: JCISE-13-1285; doi: 10.1115/1.4026510 History: Received December 17, 2013; Revised January 09, 2014

This paper presents a large-scale empirical study of OpenIDEO, an online collaborative design community. Using network analysis techniques, we describe the properties of this collaborative design network and discuss how it differs from common models of network formation seen in other social or technological networks. One major finding is that in OpenIDEO's social network the highly connected members talk more to less connected members than each other—a behavior not commonly found in other social and collaborative networks. We discuss how some of the interventions and incentives inherent in OpenIDEO's platform might cause this unique structure, and what advantages and disadvantages this structure has for coordinating distributed design teams. Specifically, its core-periphery structure is robust to network changes, but is at risk of decreasing design exploration ability if the core becomes too heavily clustered or loses efficiency. We discuss possible interventions that can prevent this outcome: encouraging core members to collaborate with periphery nodes, and increasing the diversity of the user population.

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Copyright © 2014 by ASME
Topics: Design , Collaboration
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Figures

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Fig. 2

The concept graphs have higher diameter (b) and lower density (c) than the social graphs, despite roughly equivalent network sizes (a). This is possible due to small levels of clustering within the concept graph, and the fact that the social graph has certain mechanisms built in that reduce the graph diameter (see Sec. 4). The concept graph exhibits low centralization (e) and low global efficiency (f), while the social graph exhibits medium centralization and low efficiency. In both cases, higher efficiency would be more advantageous in order to ease transfer of ideas and feedback, respectively. Figure 1 provides some visual intuition behind these results.

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Fig. 3

Degree complementary cumulative distribution functions for the largest connected component of different types of Open IDEO networks. Each line corresponds to a different challenge. Both types of networks are generally power-law distributed.

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Fig. 4

Unlike most social networks, the OpenIDEO social graph appears negatively assortative (disassortative) by degree, rather than positively assortative. This means that members with high degree (lots of communication) talk more with those with low degree, rather than with others of high degree. This style of communication is highly atypical of most social networks. It reduces the diameter of the network and increases the fraction of the members in the largest graph component. The concept graph appears neither assortative nor disassortative.

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Fig. 5

Boxplots of the number of communities detected using the k-Clique Percolation Method, for different values of k in both the concept (a) and social graphs (b) [14]. The concept graphs have a high number of small communities, while the social graphs have only a few communities that are significantly more connected. This reinforces the visual data in.

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Fig. 6

Visualizing the communities created using the k-Clique Percolation Method, for different values of k in both the social and concept graphs [14]. This uses the networks from challenge 10 as a representative example. Colored sub-graphs represent nodes within a given community, and red nodes represent nodes in multiple communities. For the concept graphs (a)–(c), multiple, non-overlapping communities are present at different community scales (k = [3,5]). However, for the social graphs there is generally only a single core community—any additional communities tend to be heavily overlapping (e.g., the red nodes in (f).

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Fig. 7

Removing OpenIDEO community managers from the social graph (“Social w/o CM”), we see some noticeable, but small changes: the centralization of the network decreases and the assortativity increases. The general behaviors we described above are unlikely to be caused exclusively by existing OpenIDEO community managers.

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