Visualizing Collective Idea Generation and Innovation Processes in Social Networks

Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino, Hiroki Sayama

Research output: Contribution to journalArticlepeer-review


Collective idea generation and innovation processes are complex and dynamic, involving a large amount of qualitative narrative information that is difficult to monitor, analyze, and visualize using traditional methods. In this study, we developed three new visualization methods for collective idea generation and innovation processes and applied them to data from online social network experiments. The first visualization is the <italic>Idea Cloud</italic>, which helps monitor collective idea posting activity and intuitively tracks idea clustering and transition. The second visualization is the <italic>Idea</italic> Geography, which helps understand how the idea space and its utility landscape are structured and how collaboration was performed in that space. The third visualization is the <italic>Idea</italic> <italic>Network</italic>, which connects idea dynamics with the social structure of the people who generated them, displaying how social influence among neighbors may have affected collaborative activities and where innovative ideas arose and spread in the social network.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Computational Social Systems
Publication statusAccepted/In press - 2022


  • Collaboration
  • Collaboration
  • collective idea generation and innovation
  • collective performance
  • Data visualization
  • exploration and exploitation
  • idea cloud
  • idea embedding
  • idea generation
  • idea geography
  • idea network
  • Monitoring
  • Numerical models
  • Social networking (online)
  • social networks
  • Task analysis
  • Technological innovation

ASJC Scopus subject areas

  • Modelling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction


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