涉及ROC和AUC的文献

• 附件::

• 备注::

• ROC

  • ROC curve
  • Response Operator Characteristic curve

• AUC

  • Area Under the Curve
  • [[SAND第6章代码]]，[[用R对网络数据进行统计分析#^e84r1qmf327|ROC曲线介绍]]

• (Arts et al., 2021)

• (Albora et al., 2021)

• (Bojanowski & Chroł, 2020)

• (Brachtendorf et al., 2020)

• (Bustos et al., 2012)

• (Gao et al., 2017)

• (Hausmann et al., 2021)

  • [[隐藏能力理论]]

• (Jie Chen et al., 2020)

• (Lee et al., 2021)

• (Ma et al., 2022)

• (Medo et al., 2018)

• (Ren et al., 2018)

• (Teodorescu, 2018)

• (Wu et al., 2019)

参考文献

Albora, G., Pietronero, L., Tacchella, A., & Zaccaria, A. (2021). Product progression: A machine learning approach to forecasting industrial upgrading. 2105.15018

Arts, S., Hou, J., & Gomez, J. C. (2021). Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures. Research Policy, 50(2), 104144. https://doi.org/10.1016/j.respol.2020.104144

Bojanowski, M., & Chroł, B. (2020). Proximity-based methods for link prediction in graphs with r package ’linkprediction’. ASK. Research & Methods, 29(1), 5–28. https://doi.org/10.18061/ask.v29i1.0002

Brachtendorf, L., Gaessler, F., & Harhoff, D. (2020). Truly standard-essential patents? A semantics-based analysis (No. 14726). C.E.P.R. Discussion Papers. https://econpapers.repec.org/paper/cprceprdp/14726.htm

Bustos, S., Gomez, C., Hausmann, R., & Hidalgo, C. A. (2012). The dynamics of nestedness predicts the evolution of industrial ecosystems. PLOS ONE, 7(11), e49393. https://doi.org/10.1371/journal.pone.0049393

Gao, M., Chen, L., Li, B., Li, Y., Liu, W., & Xu, Y. (2017). Projection-based link prediction in a bipartite network. Information Sciences, 376, 158–171. https://doi.org/10.1016/j.ins.2016.10.015

Hausmann, R., Stock, D. P., & Yıldırım, M. A. (2021). Implied comparative advantage. Research Policy, 92(1), 104143. https://doi.org/10.1016/j.respol.2020.104143

Jie Chen, Jialin Chen, Shu Zhao, Yanping Zhang, Jie Tang, Chen, J., Chen, J., Zhao, S., Zhang, Y., & Tang, J. (2020). Exploiting word embedding for heterogeneous topic model towards patent recommendation. Scientometrics, 39(1), 1–18. https://doi.org/10.1007/s11192-020-03666-4

Lee, J., Ko, N., Yoon, J., & Son, C. (2021). An approach for discovering firm-specific technology opportunities: Application of link prediction to f-term networks. Technological Forecasting and Social Change, 168, 120746. https://doi.org/10.1016/j.techfore.2021.120746

Ma, J., Pan, Y., & Su, C.-Y. (2022). Organization-oriented technology opportunities analysis based on predicting patent networks: A case of alzheimer’s disease. Scientometrics, 1–21. https://doi.org/10.1007/s11192-021-04219-z

Medo, M., Mariani, M. S., & Lü, L. (2018). Link prediction in bipartite nested networks. Entropy, 20(10), 777. https://doi.org/10.3390/e20100777

Ren, Z.-M., Zeng, & Zhang, Y.-C. (2018). Structure-oriented prediction in complex networks. Physics Reports, 750, 1–51. https://doi.org/10.1016/j.physrep.2018.05.002

Teodorescu, M. (2018). Knowledge flows and IP within and across firms - economics and machine learning approaches [PhD thesis, Harvard Business School]. https://dash.harvard.edu/handle/1/41940978

Wu, L., Wang, D., & Evans, J. A. (2019). Large teams develop and small teams disrupt science and technology. Nature, 566(7744), 378–382. https://doi.org/10.1038/s41586-019-0941-9

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