Browse > Article
http://dx.doi.org/10.3743/KOSIM.2017.34.4.007

Deep Learning Research Trends Analysis with Ego Centered Topic Citation Analysis  

Lee, Jae Yun (명지대학교 문헌정보학과)
Publication Information
Journal of the Korean Society for information Management / v.34, no.4, 2017 , pp. 7-32 More about this Journal
Abstract
Recently, deep learning has been rapidly spreading as an innovative machine learning technique in various domains. This study explored the research trends of deep learning via modified ego centered topic citation analysis. To do that, a few seed documents were selected from among the retrieved documents with the keyword 'deep learning' from Web of Science, and the related documents were obtained through citation relations. Those papers citing seed documents were set as ego documents reflecting current research in the field of deep learning. Preliminary studies cited frequently in the ego documents were set as the citation identity documents that represents the specific themes in the field of deep learning. For ego documents which are the result of current research activities, some quantitative analysis methods including co-authorship network analysis were performed to identify major countries and research institutes. For the citation identity documents, co-citation analysis was conducted, and key literatures and key research themes were identified by investigating the citation image keywords, which are major keywords those citing the citation identity document clusters. Finally, we proposed and measured the citation growth index which reflects the growth trend of the citation influence on a specific topic, and showed the changes in the leading research themes in the field of deep learning.
Keywords
deep learning; machine learning; research trends analysis; ego centered topic citation analysis; coauthorship networks; co-citation analysis; citation image keywords; citation growth index;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
연도 인용수 순위
1 Braam, R. R., Moed, H. F., & van Raan, A. F. J. (1991). Mapping of science by combined co-citation and word analysis. I. Structural aspects. Journal of the American Society for Information Science, 42(4), 233-251. http://doi.org/10.1002/(SICI)1097-4571(199105)42:4<233::AID-ASI1>3.0.CO;2-I   DOI
2 Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.   DOI
3 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444. http://doi.org/10.1038/nature14539   DOI
4 Lee, Jae Yun, & Choi, Sanghee (2011). Intellectual structure and infrastructure of informetrics. Journal of the Korean Society for Information Management, 28(2), 11-36. http://doi.org/10.3743/KOSIM.2011.28.2.011   DOI
5 Lee, J. Y., Kim, H., & Kim, P. J. (2010). Domain analysis with text mining: Analysis of digital library research trends using profiling methods. Journal of Information Science, 36(2), 144-161. http://dx.doi.org/10.1177/0165551509353251   DOI
6 이재윤 (2012). 폭소노미 연구 문헌에 대한 자아 중심 주제 인용 분석. 정보관리학회지, 29(4), 295-312. http://doi.org/10.3743/KOSIM.2012.29.4.295 (Lee, Jae Yun (2012). Ego-centered topic citation analysis on folksonomy research documents. Journal of the Korean Society for Information Management, 29(4), 295-312. http://doi.org/10.3743/KOSIM.2012.29.4.295)   DOI
7 이재윤 (2014). 공동연구 네트워크 분석을 위한 중심성 지수에 대한 비교 연구. 정보관리학회지, 31(3), 153-179. http://doi.org/10.3743/KOSIM.2014.31.3.153 (Lee, Jae Yun (2014). A comparative study on the centrality measures for analyzing research collaboration networks. Journal of the Korean Society for Information Management, 31(3), 153-179. http://doi.org/10.3743/KOSIM.2014.31.3.153)   DOI
8 Van Eck, N. J., & Waltman, L. (2014). CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, 8(4), 802-823. http://doi.org/10.1016/j.joi.2014.07.006   DOI
9 White, H. D. (2000). Toward ego-centered citation analysis. In B. Cronin & H. B. Atkins (Eds.), The web of knowledge: A festschrift in honor of Eugene Garfield (pp. 475-496). Medford, NJ: Information Today, Inc.
10 이재윤 (2013). tnet과 WNET의 가중 네트워크 중심성 지수 비교 연구. 정보관리학회지, 30(4), 241-264. http://doi.org/10.3743/KOSIM.2013.30.4.241 (Lee, Jae Yun (2013). A comparison study on the weighted network centrality measures of tnet and WNET. Journal of the Korean Society for Information Management, 30(4), 241-264. http://doi.org/10.3743/KOSIM.2013.30.4.241)   DOI
11 이재윤 (2015). 가중 네트워크를 위한 일반화된 지역중심성 지수. 정보관리학회지, 32(2), 7-23. http://doi.org/10.3743/KOSIM.2015.32.2.007 (Lee, Jae Yun (2015). A generalized measure for local centralities in weighted networks. Journal of the Korean Society for Information Management, 32(2), 7-23. http://doi.org/10.3743/KOSIM.2015.32.2.007)   DOI
12 이재윤, 김수정 (2016). 국내 재난 관련 연구 동향에 대한 계량정보학적 분석. 정보관리학회지, 33(3), 103-124. http://doi.org/10.3743/KOSIM.2016.33.3.103 (Lee, Jae Yun, & Kim, Soojung (2016). A bibliometric analysis of research trends on disaster in Korea. Journal of the Korean Society for Information Management, 33(3), 103-124. http://doi.org/10.3743/KOSIM.2016.33.3.103)
13 이재윤, 김판준, 강대신, 김희정, 유소영, 이우형 (2011). 계량서지적 기법을 활용한 LED 핵심 주제영역의 연구 동향 분석. 정보관리연구, 42(3), 1-26. http://doi.org/10.1633/JIM.2011.42.3.001 (Lee, Jae Yun, Kim, Pan-Jun, Kang, Dae-Shin, Kim, Hee-Jung, Yu, So-Young, & Lee, Woo-Hyoung (2011). A bibliometric analysis on LED research. Journal of Information Management, 42(3), 1-26. http://doi.org/10.1633/JIM.2011.42.3.001)
14 유소영 (2015). 자아 중심 네트워크 분석과 동적 인용 네트워크를 활용한 토픽모델링 기반 연구동향 분석에 관한 연구. 정보관리학회지, 32(1), 153-169. http://doi.org/10.3743/KOSIM.2015.32.1.153 (Yu, So-Young (2015). Combining ego-centric network analysis and dynamic citation network analysis to topic modeling for characterizing research trends. Journal of the Korean Society for Information Management, 32(1), 153-169. http://doi.org/10.3743/KOSIM.2015.32.1.153)   DOI
15 이재윤, 유종덕, 김희전 (2009). 텍스트마이닝을 이용한 건축학 분야의 지적 구조 분석. 경기대학교대학원 논문집, 39, 1-21. (Lee, Jae Yun, Ryoo, Jong-duk, & Kim, Hee-jeon (2009). A study on the intellectual structure of architectural studies with text mining. Journal of the Graduate School of Kyonggi University, 39, 1-21.)
16 장윤미 (2013). 자아 중심 주제 인용 분석에 의한 독서치료 주제 분야 지적구조에 관한 연구. 제20회 한국정보관리학회 학술대회 논문집, 37-41. (Chang, Yun-Mee (2013). A study on the intellectual structure of bibliographic therapy by egocentered topic citation analysis. Proceedings of the 20th Annual Conference of the Korean Society for Information Management, 37-41.)
17 김도미 (1993). 저자동시인용 분석과 인용한 문헌의 색인어 분석에 의한 지적구조의 규명: 경제학 분야를 대상으로. 정보관리연구, 24(1), 32-57. (Kim, Do Mi (1993). A study on intellectual structure using author co-citation analysis and indexing term analysis of citing documents: Application to economics. Journal of Information Management, 24(1), 32-57.)
18 김하수, 손현정, 이재윤, 강범일 (2013). 정치와 언어의 관계에 대한 양적 분석 시론. 담화와인지, 20(1), 79-111. https://doi.org/10.15718/discog.2013.20.1.79 (Kim, Ha-Soo, Son, Hyunjung, Lee, Jae Yun, Kang, Beomil (2013). A quantitative approach to the relation between politics and language. Discourse and Cognition, 20(1), 79-111. https://doi.org/10.15718/discog.2013.20.1.79)
19 딥러닝 (2014). Wikipedia. Retreived from https://ko.wikipedia.org/wiki/딥_러닝(Deep learning (2014). Wikipedia. Retreived from https://ko.wikipedia.org/wiki/딥_러닝)
20 이재윤 (2006). 계량서지적 네트워크 분석을 위한 중심성 척도에 관한 연구. 한국문헌정보학회지, 40(3), 191-214. http://doi.org/10.4275/KSLIS.2006.40.3.191 (Lee, Jae Yun (2006). Centrality measures for bibliometric network analysis. Journal of the Korean Society for Library and Information Science, 40(3), 191-214. http://doi.org/10.4275/KSLIS.2006.40.3.191)   DOI
21 이재윤 (2007). 국내 광역 과학 지도 생성 연구. 정보관리학회지, 24(3), 363-383. http://doi.org/10.3743/KOSIM.2007.24.3.363 (Lee, Jae Yun (2007). Making a science map of Korea. Journal of the Korean Society for Information Management, 24(3), 363-383. http://doi.org/10.3743/KOSIM.2007.24.3.363)   DOI