Abstract
The paper has demonstrated that the increasingly online relationship between designers and their digital tools can be quantitatively represented, described and analyzed through the data-mining of design-domain specific and tool-based social network (i.e. Grasshopper3D). It explores design trends' correlations based on network user groups' size, users' demographics, nodes' degree centrality and discussion threads' popularity.
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CITATION STYLE
Koh, I. (2018). Learning design trends from social networks: Data mining, analysis & visualization of grasshopper® online user community. In CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting (Vol. 2, pp. 277–286). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). https://doi.org/10.52842/conf.caadria.2018.2.277
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