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Research Papers: Design Automation

Network Analysis of Design Automation Literature

[+] Author and Article Information
Tinghao Guo

Industrial and Enterprise Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 60801
e-mail: guo32@illinois.edu

Jiarui Xu

Language Technologies Institute,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: jiaruix@cs.cmu.edu

Yue Sun

Computer Science,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: yuesun3 @illinois.edu

Yilin Dong

Computer Science,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: ydong24@illinois.edu

Neal Davis

Computer Science,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: davis68@illinois.edu

James T. Allison

Industrial and Enterprise Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 60801
e-mail: jtalliso@illinois.edu

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.

J. Mech. Des 140(10), 101403 (Jul 31, 2018) (13 pages) Paper No: MD-17-1408; doi: 10.1115/1.4040787 History: Received June 17, 2017; Revised June 12, 2018

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.

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Figures

Grahic Jump Location
Fig. 1

Main topic frequency plot

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Fig. 2

Subtopic frequency plot

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Fig. 3

Main topic chronological evolution

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Fig. 4

Subtopic chronological evolution

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Fig. 5

Main topic correlation

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Fig. 6

Subtopic correlation

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Fig. 7

Main topic and subtopic correlation

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Fig. 8

Average in-degree for main topics

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Fig. 9

Average in-degree for subtopics

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Fig. 10

Citation network for sustainable energy system design

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Fig. 11

Scatter plot for association rules of the main topics

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Fig. 12

Group matrix-based visualization for main topics

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Fig. 13

Two significant clusters identified using PM: (a) cluster 4: design for market systems and (b) cluster 5: visualization

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