In this paper, we present the results of a study of citation and co-authorship networks for articles published at the ASME Design Automation Conference (DAC) during the years 2002–2015. Two topic-modeling methods are presented for studying the DAC literature: A frequency-based model was developed to explore DAC topic distribution and evolution, as well as citation analysis for each core topic. Correlation analysis and association-rule mining were used to discover relationships between topics. A new unsupervised learning algorithm, propagation mergence (PM), was created to address identified shortcomings of existing methods and applied to study the existing DAC citation network. Influential articles and important article clusters were identified and effective visualizations created. We also investigated the DAC co-authorship network by identifying key authors and showing that the network structure exhibits small-world-network properties. The resulting insights, obtained by the both the proposed and existing methods, may be beneficial to the engineering design research community, especially with respect to determining future research directions and possible actions for improvement. The data set used here is limited; expanding to include additional relevant conference proceedings and journal articles in the future would offer a more complete understanding of the engineering design research literature.
Network Analysis of Design Automation Literature
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 17, 2017; final manuscript received June 12, 2018; published online July 31, 2018. Assoc. Editor: Carolyn Seepersad.
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Guo, T., Xu, J., Sun, Y., Dong, Y., Davis, N., and Allison, J. T. (July 31, 2018). "Network Analysis of Design Automation Literature." ASME. J. Mech. Des. October 2018; 140(10): 101403. https://doi.org/10.1115/1.4040787
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