在文献““Betweenness Centrality” as an Indicator of the“Interdisciplinarity” of Scientific Journals”(http://www.leydesdorff.net/betweenness/index.htm),Loet说了一段这样的话:
Normalization of the matrix for the size of patterns of citation can suppress this effect (Bonacich, personal communication, 22 May 2006). Fortunately, there is increasing consensus that normalization in terms of the cosine and using the vector-space model provides the best option in the case of sparse citation matrices (Ahlgren et al., 2003; Chen, 2006; Salton & McGill, 1983).
我对这段话及其上下文的理解是,采用余弦范化矩阵,可以降低原始数据波动的影响。在原始矩阵上,绝对值上的差异可能太过明显,筛选时会过滤掉太多的信息。而采用相关性矩阵,能将差异相对缩小,以避免泼水连孩子都泼出去?Loet后面指出很多学者都在用这种方法(似乎感觉这样引用证明不太妥当,别人用我就用???还是我对这段话理解有误?)目前我能找到的其它Loet的论文,关于余弦范化矩阵就是介绍了该方法,似乎未做太多讨论。
此外,Loet还和Egghe做过关于余弦相关性与皮尔逊相关性的讨论“The relation between Pearson’s correlation coefficient r and Salton’s cosine measure”(http://www.leydesdorff.net/cosinevspearson/index.htm)。末端结论的意思似乎是,当相关性阈值高于一定水平,两者的结果相关性很强?