[Purpose/significance] The identification of key research directions (KRDs) is significant to the management of scientific research and the formulation of policy. Existing methods of quantitative analysis mainly used indicators based on novelty, growth and other characteristics to identify and recommend KRDs. This paper used the relationships of research directions further and identified KRDs from network topology and characteristic two dimensions. [Method/process] On the basis of the building of citation network of papers in a field, a large-scale network clustering algorithm was used to detect research directions, and the topic association network was built. Then, identification algorithms of important nodes in complex networks were used to identify KRDs from the dimension of network topology, and an index system of selecting KRDs was constructed by combining three indicators of characteristic dimension, including novelty index, growth index and H-index. [Result/conclusion] After an empirical analysis of the field of nanotechnology, with the experts' interpretation, the paper concluded that growth index, Gefura and weighted PageRank are more objective and stable. Finally, 208 KRDs were identified by synthetically using above three indicators.
[1] SMALL H. A co-citation model of a scientific specialty:a longitudinal study of collagen research[J]. Social studies science, 1977, 7(2):139-166.
[2] SMALL H, SWEENEY E. Clustering the science citation index? using co-citations. I. A comparison of methods[J]. Scientometrics, 1985, 7(3-6):391-409.
[3] SMALL H, SWEENEY E, GREENLEE E. Clustering the science citation index using co-citations. Ⅱ. Mapping science[J]. Scientometrics, 1985, 8(5/6):321-340.
[4] UPHAM S, SMALL H. Emerging research fronts in science and technology:patterns of new knowledge development[J]. Scientometrics, 2010, 83(1):15-38.
[5] CHEN C. CiteSpace Ⅱ:detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006, 57(3):359-377.
[6] CHEN C, CHEN Y, HOROWITZ M, et al. Towards an explanatory and computational theory of scientific discovery[J]. Journal of informetrics, 2009, 3(3):191-209.
[7] CHEN C, HU Z, LIU S, et al. Emerging trends in regenerative medicine:a scientometric analysis in CiteSpace[J]. Expert opinion on biological therapy, 2012, 12(5):593-608.
[8] BOYACK K, KLAVANS R, SMALL H, et al. Characterizing the emergence of two nanotechnology topics using a contemporaneous global micro-model of science[J]. Journal of engineering & technology management, 2014, 32(32):147-159.
[9] 潘教峰, 张晓林, 王小梅, 等. 科学结构地图2012[M]. 北京:科学出版社, 2013.
[10] 王小梅, 韩涛, 王俊, 等. 科学结构地图2015[M]. 北京:科学出版社, 2015.
[11] 中国科学院科技战略咨询研究院. 2017研究前沿[EB/OL].[2018-03-12]. http://www.casisd.cn/zkcg/zxcg/201711/P020171121342129165876.pdf.
[12] MORRIS S, YEN G, WU Z, et al. Time line visualization of research fronts[J]. Journal of the American Society for Information Science and Technology, 2003, 54(5):413-422.
[13] HOPCROFT J, KHAN O, KULIS B, et al. Tracking evolving communities in large linked networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(S1):5249-5253.
[14] HUANG M, CHANG C. Detecting research fronts in OLED field using bibliographic coupling with sliding window[J]. Scientometrics, 2014, 98(3):1721-1744.
[15] GARFIELD E. Citation indexes in sociological and historical research[J]. Journal of the American Society for Information Science and Technology, 1963, 14(4):289-291.
[16] GARFIELD E, PUDOVKIN A, ISTOMIN V. Why do we need algorithmic historiography?[J]. Journal of the American Society for Information Science and Technology, 2003, 54(5):400-412.
[17] GARFIELD E. Historiographic mapping of knowledge domains literature[J]. Journal of information science, 2004, 30(2):119-145.
[18] SHIBATA N, KAJIKAWA Y, TAKEDA Y, et al. Detecting emerging research fronts based on topological measures in citation networks of scientific publications[J]. Technovation, 2008, 28(11):758-775.
[19] SHIBATA N, KAJIKAWA Y, TAKEDA Y, et al. Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications[J]. Technological forecasting & social change, 2009, 78(2):274-282.
[20] SMALL H, BOYACK K, KLAVANS R. Identifying emerging topics in science and technology[J]. Research policy, 2014, 43(8):1450-1467.
[21] GLÄNZEL W, THIJS B. Using ‘core documents’ for detecting and labelling new emerging topics[J]. Scientometrics, 2012, 91(2):399-416.
[22] ARORA S, PORTER A, YOUTIE J, et al. Capturing new developments in an emerging technology:an updated search strategy for identifying nanotechnology research outputs[J]. Scientometrics, 2013, 95(1):351-370.
[23] ECK N, WALTMAN L. CitNetExplorer:a new software tool for analyzing and visualizing citation networks[J]. Journal of informetrics, 2014, 8(4):802-823.
[24] WALTMAN L, ECK N. A smart local moving algorithm for large-scale modularity-based community detection[J]. European physical journal B, 2013, 86(11):471.
[25] LV L, ZHANG Y, HO Y, et al. Leaders in social networks, the delicious case[J]. Plos one, 2011, 6(6):e21202.
[26] BRIN S, PAGE L. The anatomy of a large-scale hypertextual web search engine[J]. Computer networks and ISDN system,1998,30(1-7):107-117.
[27] GUNS R, ROUSSEAU R. Unnormalized and normalized forms of gefura measures in directed and undirected networks[J]. Frontiers of information technology & electronic engineering, 2015, 16(4):311-320.
[28] HIRSCH J. An index to quantify an individual's scientific research output[J]. Proceedings of the national academy of sciences of the United States of America, 2005, 102(46):16569-16572.
