分类:引文热点追踪
来自Big Physics
之前我们研究了发文的热点追踪现象[1][2](见热点追踪),也就是是否当前更大更热(需要在比较近的一段时间内算累计)的领域会吸引更多的新论文,也就是看新论文所在的领域的小大分布[math]\displaystyle{ \frac{m_{k}\left(t, t+\Delta t\right)}{\sum_{k} m_{k}\left(t, t+\Delta t\right)} }[/math]和当前的领域大小的分布函数[math]\displaystyle{ \frac{n_{k}\left(t_{0}, t\right)}{\sum_{k} n_{k}\left(t_{0}, t\right)} }[/math]的比。具体计算上,可以直接拟合函数[math]\displaystyle{ \frac{\frac{m_{k}\left(t, t+\Delta t\right)}{\sum_{k} m_{k}\left(t, t+\Delta t\right)}}{\frac{n_{k}\left(t_{0}, t\right)}{\sum_{k} n_{k}\left(t_{0}, t\right)}} }[/math](它是[math]\displaystyle{ k }[/math]的函数,一般是[math]\displaystyle{ k^{\alpha} }[/math]的形式);也可以分别用Newman的幂律分布函数拟合方法引用错误:没有找到与</ref>对应的<ref>标签
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- ↑ 引用错误:无效
<ref>标签;未给name属性为Wu:HotnessSR的引用提供文字 - ↑ 2.0 2.1 Menghui Li, Liying Yang, Huina Zhang, Zhesi Shen, Chensheng Wu, Jinshan W, Do Mathematicians, Economists and Biomedical Scientists Trace Large Topics More Strongly Than Physicists?,Journal of Informetrics,10.1016/j.joi.2017.04.004.
- ↑ Hanel R, Corominas-Murtra B, Liu B, Thurner S (2018) Correction: Fitting power-laws in empirical data with estimators that work for all exponents. PLOS ONE 13(4): e0196807. https://doi.org/10.1371/journal.pone.0196807
- ↑ A. Clauset, C.R. Shalizi, and M.E.J. Newman, "Power-law distributions in empirical data" SIAM Review 51(4), 661-703 (2009).
- ↑ Y. Virkar and A. Clauset, Power-law distributions in binned empirical data. Annals of Applied Statistics 8(1), 89 - 119 (2014).
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