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research-article

Network Analysis of Design Automation Literature

[+] Author and Article Information
Tinghao Guo

Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 60801, USA
guo32@illinois.edu

Jiarui Xu

Language Technologies Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
jiaruix@cs.cmu.edu

Yue Sun

Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
yuesun3@illinois.edu

Yilin Dong

Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
ydong24@illinois.edu

Neal Davis

Teaching Assistant Professor, Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
davis68@illinois.edu

James T. Allison

Assistant Professor, Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
jtalliso@illinois.edu

1Corresponding author.

ASME doi:10.1115/1.4040787 History: Received June 17, 2017; Revised June 12, 2018

Abstract

In this article, 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.

Copyright (c) 2018 by ASME
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