NetOpen21: modeling open innovation communities with network science

What is Net-open?

“NetOpen21: modeling open innovation communities with network science” is a half-day satellite to the Network 2021 conference (6-10 July 2021) happening on June 30th, 8:30am-12:30pm EST. It aims to bring together an interdisciplinary group of theoreticians, data and network scientists, social scientists and open innovation practitioners (among others) to interrogate and investigate how large-scale, fine-grain datasets of open science, citizen science and open-source ecosystems allow to measure and model collaborative processes underlying collective performance in science and engineering.

The satellite will alternate between invited and contributed talks. The satellite will be held entirely online. Participants will need to register to the main conference, including 2 weeks of satellite events. Thanks to the support of the OCEAN program, we will be able to provide financial support for contributors, to make sure everyone can participate!

Description of the session

Contemporary science and innovation is a team endeavor. Not only are 90% of scientific papers co-authored, collaboration is associated with higher impact too. Teams can organize the energy and ideas of collaborators so that their whole is greater than the sum of its parts. Therefore, there is tremendous desire to scale these benefits through mass collaboration, harnessing the crowd through open science and innovation. For example, the open-source and open science movements have demonstrated that communities of 1,000+ can contribute to a common goal in a short time. Yet collaboration at these scales creates unique challenges, from communication to coordination, which, if left unaddressed, could jeopardize the completion and the success of the projects. In particular, the COVID-19 pandemic has shown the challenge of coordinating large-scale, self-organized, open collaborative initiatives to provide an advanced collective response to the emergency. Understanding how collaboration network structure underlies performance is thus key for the design of organizational strategies as well as the development of new technologies for making groups more effective and collective actions more scalable. 

Prompted by the prevalence of the phenomenon, a series of studies have explored how team composition, organization or dynamics determine team performance and survival, usually relying on conceptual models or proxies of interaction data. In parallel, the sociology of science has offered numerous in-depth insights from case studies, revealing the multiple factors that influence the way a group of individuals work together both in closed and open settings towards a common goal. Despite these advances, we are still lacking fine-grained quantitative insights and network growth models on i) the micro-level, intra-team interaction processes underlying project performance and ii) the macro-level, inter-teams collaborations underlying the scalability and resilience of team ecosystems in open settings. 

The purpose of this satellite is to bring together an interdisciplinary group of theoreticians (in particular from Statistical Physics and Ecological modeling), data and network scientists (with expertise in open-source and open science communities data such as GitHub, SciStarter, or Zooniverse), social scientists (with expertise in knowledge and collaboration networks) and open innovation practitioners (community coordinators, open program leaders) to interrogate and investigate how large-scale, fine-grain datasets of open science, citizen science and open-source ecosystems allow to measure and model collaborative processes underlying collective performance in science and engineering. Overall, we expect such insights will help outline a sustainable framework to design open organizations with high ecosystemic resilience able to cope with future challenges requiring resilience and rapid collective actions at scale, both in public and private domains.


The satellite will take place online from 8:30am to 12:30pm EST (Eastern Time, UTC-4), or 2:30pm-6:30pm CET. Times below are EST.

  • 8:30am EST – Introduction to open innovation data and models
  • Session I: Theories of organization of science and engineering in open collaborative projects (focus on data sources and representations)
    • 8:40am – 9:15am: Henry Sauermann (ESMT Berlin), “Crowds, Citizens, and Science”
    • 9:15am – 9:50am: Laura Dabbish (Carnegie Mellon), TBA
  • Break (10 min)
  • Session II: Methods to describe open science communities and collaborations
    • 10am – 10:20am: Bradly Alicea (Orthogonal Research), “Network Architectures for Growing Academic Open-Source”
    • 10:20am – 10:40am: Olga Kokshagina (RMIT University), “Assembling The Collaboration Puzzle: Integrating Dispersed Knowledge On Individual-Level Antecedents Of Scientific Collaborations With Non-Professional Scientists”
    • 10:40am – 10:50am: Michael Weiss (Carleton University), “Analysis of Developer Social Networks During Bug Triaging”
  • Break (10 min)
  • Session III: Models to study open organisation networks (focus on theoretical models and their applications)
    • 11am – 11:35am: Jim Bagrow (UVM), “Exploring the OCEAN: Open source Complex Ecosystems and Networks”
    • 11:35am – 12:10pm: Laurent Hébert-Dufresnes (UVM), “Multidimensional localization and information silos in networks”
  • 12:10pm – 12:30pm: Final discussions (20 min)


Marc Santolini (CRI, Universite de Paris, INSERM)
Liubov Tupikina (Bell Labs)
Robbie Ward (GeorgiaTech)
Juniper Lovato (University of Vermont)

For more information about this satellite, please contact us.

About the speakers

Bradly Alicea

Bradly Alicea has a PhD from Michigan State University. He has published in multiple academic fields, and in venues including Nature Reviews Neuroscience, Stem Cells and Development, Biosystems, PLoS Biology, and Proceedings of Artificial Life. He is currently Head Scientist and Founder of Orthogonal Research (, Senior Contributor at the OpenWorm Foundation (, and Open-source Community Manager at the Rokwire Initiative (University of Illinois). Please see Bradly’s research website ( or research blog ( for more information.

Jim Bagrow

James Bagrow is an Associate Professor of Mathematics & Statistics at the University of Vermont and a member of the Vermont Complex Systems Center. Before joining Vermont, he was a postdoctoral researcher at the Center for Complex Networks Research at Northeastern University and a research assistant professor at Northwestern University. Professor Bagrow received his Ph.D. in Physics from Clarkson University in 2008. He is interested in understanding the underlying rules and organizing principles of complex physical and social systems. His work combines mathematical models with large-scale data analysis to better understand these systems, with a particular emphasis on network science and human dynamics. Other interests include data science, stochastic and nonlinear dynamics, dynamical systems, and novel optimization and machine learning methods.

Laura Dabbish

Laura Dabbish is an associate professor in the HCII and Heinz College at CMU.

Laura studies the social effects of digital transparency. Her research connects social and organizational psychology with technology and design. An emerging Internet trend is greater digital transparency, such as the use of real names in social networking sites, feeds of friends’ activities traces of content re-use, and visualizations of team interactions. This transparency could radically improve collaboration and learning. It also presents new challenges around privacy and identity management. Her goal is to understand the social benefits and tradeoffs of digital transparency for users.

She collaborates with researchers across a variety of fields, including social psychology, organizational behavior, sociology, cyber-security, networking, software development and information systems to understand this multifaceted socio-technical phenomenon. They study the nature of interaction in existing settings with high levels of digital transparency, such as online professional social networking communities, and experiment with new designs to make workflows, content provenance, and work histories transparent.

Laurent Hébert-Dufresne

Laurent Hébert-Dufresne has joined UVM CEMS in January 2018. He is a James S. McDonnell Fellow at Santa Fe Institute, specializing in the research of network theory and nonlinear dynamics. He received his BSc, MSc and PhD in physics from the Université Laval in Québec. Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.

Olga Kokshagina

Olga Kokshagina is an Assistant Professor and Vice Chancellor’s Research Fellow in the Graduate School ofBusiness and Law at RMIT University. She is affiliated with the Centre for Management Science at MinesParisTech PSL Research University, France. Her work is related to several areas: innovation and technologymanagement, open science, open & radical innovation, data-driven innovation. She is a co-director of RMITW+SN network and a member of the French Digital Council (CNNUM).

Henry Sauermann

Henry joined ESMT Berlin in May 2017. He is the first holder of the POK Pühringer PS Chair in Entrepreneurship. Since January 2018 Henry has been the Director of the Institute for Endowment Management and Entrepreneurial Finance (IFEE). Previously he was an associate professor of strategy and innovation and the PhD Coordinator at the Scheller College of Business at the Georgia Institute of Technology.

Henry explores the role of human capital in science, innovation, and entrepreneurship. Among others, he studies how scientists’ motives and incentives relate to important outcomes such as innovative performance in firms, patenting in academia, or career choices and entrepreneurial interests. In new projects, Henry studies the dynamics of motives and incentives over time, and explores non-traditional innovative institutions such as Crowdsourcing and Citizen Science. Additional work is underway to gain deeper insights into scientific labor markets and to derive implications for junior scientists, firms, and policy makers.

Henry was appointed as a research associate at the U.S. National Bureau of Economic Research (NBER). His work has been funded by the National Science Foundation, the Kauffman Foundation, a Sloan Foundation Research Program, as well as the Georgia Research Alliance. He has published in a wide range of journals including Management Science, Organization Science, Research Policy, Strategic Entrepreneurship Journal, Science, the Proceedings of the National Academy of Sciences (PNAS), and Science Advances. He has presented his work at many national and international conferences and was invited to share his research with policymakers and business executives at meetings of The National Academies and The Conference Board. Henry is Associate Editor at the Strategic Entrepreneurship Journal, Advisory Editor at Research Policy, and Editorial Board Member at the Strategic Management Journal.

Michael Weiss

Michael Weiss is a faculty member of the Technology Innovation Management program at Carleton University. His research interests include open source software, ecosystems, social network analysis, entrepreneurship, and business analytics.

Contributed abstracts

Assembling The Collaboration Puzzle: Integrating Dispersed Knowledge On Individual-Level Antecedents Of Scientific Collaborations With Non-Professional Scientists
Olga Kokshagina

Across disciplines, the production of scientific knowledge increasingly involves collaboration and co-creation with individuals not primarily employed in the academic science system (e.g., patients, indigenous people, managers). Such collaborations are claimed to yield promising benefits for science (e.g., more novel research) and for society (e.g., democratization of science). Yet, the discussions about antecedents for successful collaborations remain within the respective disciplinary boundaries, creating dispersed piles of valuable but disconnected insights due to many idiosyncrasies in terms of languages used, stakeholders involved, and forms of collaborations applied and investigated. This hampers the development of an integrated, cross-disciplinary understanding of successful collaborations.

Our integrative review addresses this challenge by focusing on an often-presumed and diverse source of collaboration success—the individuals involved—and classifying collaborations along three theory-grounded, unifying dimensions: collaborators’ field proximity, number of different stakeholder groups involved, and their participation in professional vs. personal roles. We systematically reviewed 33,216 papers retrieved through a keyword search in Scopus and EBSCOhost across all disciplines, out of which 78 met the criteria of testing, proposing, or reflecting upon individual-level antecedents for scientific collaborations. Our analysis reveals 103 individual-level factors in 19 categories influencing three outcome dimensions: initial propensity to engage in collaborations, collaborators behavior, and (perceived) collaboration success. The resulting integrative framework further distinguishes between individual-level drivers of and barriers for scientists and non-professional scientists as well as those resulting from their interaction. Our findings contribute to the configuration of a new heuristic for collaborative knowledge production processes and the organization of science.

Analysis of Developer Social Networks During Bug Triaging
Edwin Omoigui and Michael Weiss

Previous research has used social network analysis for discovering potential developer collaborations, but few researchers have used social network analysis in the context of bug triaging. In this work, we use social network analysis to understand the developer network using historic contribution patterns and identify core contributors or subject matter experts on different types of bugs.

Bugs in an open source project must be triaged to determine which bug requires most attention and which developers will be assigned the responsibility of resolving a bug (Anvik et al., 2006). Often, developers tend to choose what bugs to resolve, which may result in leaving certain bug-fix tasks unattended (Licorish et al., 2017). It may also lead to an inefficient assignment of bugs to developers, which can result in unnecessary bug “tossing”, during which a bug is reassigned to other developers. The outcomes of this research can be used to develop better ways to help assign bugs to developers.

We mine the commit log of an open source project to gather information on the types of bugs developers contribute to, and derive a developer collaboration network. Using social network analysis we can then identify key developer groupings. If two developers both commit to a common or similar bug report, it is indicative of a link between them in the developer network (Zhang et al., 2014). Those groupings allow us to articulate the distinction between types of developers (core and more peripheral developers) in terms of the range and fequency of bugs they handle.

Bugs can be of different types. They include fixes, as well as requests for new features or enhancements. Bugs are tracked in a bug tracking system, and when a bug has been fixed or the enhancement has been made, by convention the commit message often includes the type of change, for example, a bug fix will be labeled by prefixing the commit message with [FIX].

The project we studied has a sizable community of over 150 developers and a long duration (20 years). Over the lifetime of the project, more than 60,000 commits were made to the project repository in 13 bug categories. This presents a unique opportunity to study not only the groupings of developers at a given point in time, but also their evolution, as developers leave and new developers join the project.

From the commits, we obtain a bipartite network of developers and bug types. Visualizing this network allows us to observe patterns and developer contributions and groupings within the project developer network. By varying the size and color of nodes and edges we can highlight significant contributors and the amount of contributions they made. It is also instructive to visualize the evolution of this network over time. This allows us to detect changes in the contribution patterns. By projecting the network onto a network of developers we can also examine the centrality of developers, although this is not our focus here.

To distinguish core developers from peripheral developers, we group developers based on the number of bug types they contribute to. We consider core developers to be developers who contribute to a diverse range of bugs. This makes this group of developers the ones with the highest level of all-round expertise. On the other hand, more peripheral groups of developers make more tailored contributions to only specific types of bugs. The former group are also referred to as generalists in the literature (Avelino et al., 2019), and the latter as specialists. This distinction can be observed in many open source projects, and both types of role are required in a healthy project.


Anvik, J., Hiew, L., & Murphy, G. (2006). Who should fix this bug?. International Conference on Software Engineering (ICSE), 361-370. ACM.

Avelino, G., Passos, L., Hora, A., & Valente, M. (2019). Measuring and analyzing code authorship in 1+ 118 open source projects. Science of Computer Programming, 176, 14-32.

Hong, Q., Kim, S., Cheung, S. C., & Bird, C. (2011). Understanding a developer social network and its evolution. IEEE International Conference on Software Maintenance (ICSM), 323-332. IEEE.

Licorish, S., & MacDonell, S. (2017). Exploring software developers’ work practices: Task differences, participation, engagement, and speed of task resolution. Information & Management, 54(3), 364-382.

Zhang, W., Nie, L., Jiang, H., Chen, Z., & Liu, J. (2014). Developer Social Networks in Software Engineering: Construction, Analysis, and Applications. Science China Information Sciences 57(1), 1-23.

Network Architectures for Growing Academic Open-Source
(Bradly Alicea)

Open-source models of collaboration are becoming a popular mode to conduct academic-related research projects. Particularly during the pandemic, a fully-virtual and highly flexible academic community can be a critical component of a young scholar’s development. In a typical academic department or community, the structure is that of a strict hierarchy. But open-source offers an alternative: a community can form spontaneously. This spontaneous element is seen most often with respect to large fluctuations in expertise and membership, in addition to the need to address heterogeneous sets of tasks over time. While some of these challenges exist in traditional academic settings, looking at these problems in terms of open-source networks cast a new light on these phenomena. In fact, the open-source academic collaboration examples we will be examining resembles the emergence of new fields in a citation network [1] more than a traditional academic Departmental setting. This is by intent (preference for interdisciplinary collaboration), and demonstrates the flexibility of such architectures.

Thinking about the advantages of open-source organization in terms of complex networks allow us to pose the following question: how should community-building proceed to build a highly-flexible and sustainable community? As open-source communities often have a low barrier to entry and more contingent membership as compared to a traditional academic setting, governance plays an explicit role in cultivating the network architecture. I have identified three potential structures to aim for when managing such a community: densely connected networks, strictly hierarchical networks, or selective disconnection with intentional interconnectivity between different modules of the network. These represent a random strategy, a strategy that mimics traditional academic communities, and a mix of these two examples with innovation.

In this talk, I will use examples from the OpenWorm Foundation [2], Orthogonal Research and Education Laboratory [3], and Rokwire Community to answer the question of how complexity is cultivated in open-source networks. I offer three main findings from my experience of leading within these open-source organizations. The participation signature in the community is that of a single mode of core contributors, but with a long tail of contingent participants. This latter group will participate as needed or desired, and by conventional metrics do not seem to contribute much. As part of the community, however, these contributors can serve as a strategic reserve, allowing for flexibility in how different projects within the community can utilize expertise. Another aspect of this long-tailed membership is how participation can be stimulated by assigning formal roles to members based on their pre-existing skills. Roles such as editor, facilitator, or members of interest groups can create cliques within the network. While these role-based cliques serve to constrain connectivity., they also sharpen how expertise is utilized. Finally, cliques can recapitulate the diversity of a densely connected network by recruiting different types of expertise into the same clique. This leads to a high degree of local connectivity, and as people with specific skills can move from project to project, this also provides a degree of global interconnection as well.

[1] Batagelj, V., Ferligoj, A., and Squazzoni, F. (2017). The emergence of a field: a network analysis of research on peer review. Scientometrics, 113, 503–532.

[2] Sarma, G.P., Lee, C-W., Portegys, T., Ghayoomie, V., Jacobs, T., Alicea, B., Cantarelli, M.,
Currie, M., Gerkin, R.C., Gingell, S., Gleeson, P., Gordon, R., Hasani, R.M., Idili, G., Khayrulin, S., Lung, D., Palyanov, A., Watts, M., Larson, S.D. (2018). OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans. Philosophical Transactions of the Royal Society B, 373, 20170382. doi:10.1098/rstb.2017.0382.

[3] Alicea, B. (2020). Building a Distributed Virtual Laboratory Adjacent to Academia. MetaArXiv, doi:10.31222/

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