Campanario, J. M. (1995). Using neural networks to study networks of scientific journals. Scientometrics, 33(1), 23-40.

Campanario, J. M. (1995). Using neural networks to study networks of scientific journals. Scientometrics, 33(1), 23-40.

information visualization/self-organizing map

本研究以自組織映射圖呈現期刊相互引用的網絡結構，作者認為由於Kohonen自組織映射圖本身的數學形式將使得愈緊密連結的期刊映射在愈靠近的位置，並且自我引用較多的期刊在圖形上佔有較大的面積。作者進行了四個資料集的期刊引用網絡研究，包括19種化學物理期刊和20種傳播學期刊，其餘兩個資料集是不同年份的社會學期刊，分別包括11種及13種期刊，用來比較領域在不同時間區間內是否產生變化。每一種期刊表示成一個n維的特徵向量，n是資料集內的期刊種類，特徵向量上的每一個成分對應到該期刊引用另一個期刊的次數。

If one paper cites an earlier publication, they bear a conceptual relationship. The references given in a publication link that publication to previous knowledge. In the network context, information is reconceptualized in terms of social linkages and shared meanings. According to Small and Garfield(1985), citation indexes, showing millions of interconnections annually among hundreds of thousands of scientific articles and books, seem ideally suited for deriving natural maps of the scientific landscape.

The results of studies carried out with the above methodologies have been used to identify science maps (Small, Sweeney, and Greenle, 1985; Small, 1993), maps of disciplines (Garfield, 1986), research fronts (Dixon, 1989), scientific journal networks (Midorikawa, 1983; Pinski, 1977; Saito, 1990), epistemic and conceptual networks(Leydesdorff, 1991; van Raan and Tijssen, 1993), invisible colleges (Lievrouw, Rogers, Lowe, and Nadel, 1987) or author networks (McCain, 1986) and to establish the rank of journals in a given network (Doreian, 1987; Hummon and Doreian, 1989; Doreian, 1994; Bonitz, 1990).

Journals are a central institution of science because they are the primary formal channels for communicating theories, methods and empirical results to the readers of those journals (Rice, 1990).

Four sets of journal-to-journal citation data were used to apply Kohonen's map algorithm to the study of networks of scientific journals. ... Data are for citations among 19 chemical physics journals pooled for 1981 (data set I), 20 communication journals pooled from 1977 to 1985 (data set II), 11 sociology journals pooled from 1970 to 1970 (data set III) and for citations among 13 sociology journals from 1975 to 1976 (data set IV). Data sets III and
IV include data referring to the same journals (for different years) with some new journals added in data set IV. This makes it possible to compare the results of two different years that are made up of almost the same journals.

To perform the computations, each journal was coded as an n-component vector (n representing the number of journals in a given set). Each component of the vector is the number of citations given by each journal to each other journal. The input vectors were normalized to allow the algorithm to normalize the weights.

The figures show the distribution of relational space among the journals. Because of the mathematical formalism of the Kohonen maps, the most closely linked journals are located close to each other. In addition, domains occupied by the journals with a large number of self-citations tend to be greater.

Most of the multidimensional statistical methods use a symmetrical matrix of relations among cases to define a distance. However, the cross-citation matrix among journals is asymmetric, as noted earlier. This fact reflects the hierarchical structure of journal relationships: some journals are subordinate to others (Leydesdorff, 1986). This problem is sometimes overcome by computing some kind of correlation in order to obtain a symmetrical cross-citation matrix. However, this transformation causes the loss of the hierarchic quality of journal interrelations. This is manifested in the domain map in which, sometimes, a given journal activates more cells than other closely linked journals.