Chen, C., & Carr, L. (1999). Trailblazing the literature of hypertext: Author co-citation analysis (1989-1998). Proceedings of the 10th ACM Conference on Hypertext (Hypertext '99), 51-60.

Chen, C., & Carr, L. (1999). Trailblazing the literature of hypertext: Author co-citation analysis (1989-1998). Proceedings of the 10th ACM Conference on Hypertext (Hypertext '99), 51-60.

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本論文以9屆(1987-1998)的ACM Hypertext 學術研討會會議論文為研究資料，運用作者共被引分析(author co-citation analysis, ACA)、Pearson相關係數分析(Pearson’s correlation coefficients)、因素分析(factor analysis)等技術，探討超文件處理與應用學術領域的研究專長(specialties)，並利用尋徑網路尺度(Pathfinder network scaling)將研究專長分析所產生的結果進行視覺化。在這個研究裡，共分析367位引用次數較多的作者之間的共被引現象，結果共產生39個因素，這些因素共解釋了87.8%的變異數。若以前四個因素而言，則解釋了52.1%。從因素內的作者來命名，前四項超文件處理與應用學術領域的研究專長分別是經典(Classics)、資訊檢索(Information retrieval)、圖形使用者介面與資訊視覺化(Graphical user interfaces and information visualisation)以及連結與連結機制(Links and linking mechanisms)。

The ultimate goal of our work is to realise the vision of making the best use of an interrelated information space and building one’s own threads of association. As one step in this direction, we explore a new paradigm of structuring and visualising a domain-specific information space.

In this study, we choose the field of hypertext as the subject domain and map the literature of hypertext based on the ACM Hypertext conference proceedings (1987-1998).

The idea of mapping the tracks of science is explained by Garfield in [8]. The aim of such work is to identify research front specialties in a field of study. A specialty is characterised by its influence on the development of a given field. One can tell a specialty by the number of citations that it receives.

In 1981, Institute for Science Information (ISI) published ISI Atlas of Science in biochemistry and molecular biology [10]. The Atlas was constructed based on co-citation index associated with publications in the field over a limited period of one year. 102 distinct clusters of articles were identified, which were called research front specialties, in order to give researchers a snapshot of significant research activities in biochemistry and molecular biology.

White and McCain [17] used author co-citation analysis to map the field of information science. ... Their study also included a factor analysis, in which major specialties were identified. One of the most remarkable findings is that the field of information science consists of two major specialties with litter overlap between their memberships: experimental retrieval and citation analysis.

In a series of studies, we have been investigating the role of Pathfinder network scaling techniques in reducing the excessive number of links and extracting the most salient structures from a range of proximity data [3]. One problem we repeatedly encountered is an interpretation problem: users found hard to make sense the nature of links selected by Pathfinder. ... A simple and easy-to-understand method is needed to explain the structure of a Pathfinder network, especially when the nodes are high dimensional in nature.

Following [17], the raw co-citation counts were transformed into Pearson’s correlation coefficients using the factor analysis. These correlation coefficients were used to measure the proximity between authors’ co-citation profiles. ... In the factor analysis, principal component analysis with varimax rotation was used to extract factors. The default criterion, eigenvalues greater than one, was specified to determine the number of factors extracted. ... Pearson correlation matrices were submitted to the GSA environment for processing, especially including Pathfinder network scaling and VRML-scene modelling.

Thirty-nine factors were extracted from the 367 x 367 author co-citation data set. These factors explain 87.8% of the variance. In particular, the top four factors alone explain 52.1% of the variance.

Factor 1: Classics.
Factor 2: Information retrieval.
Factor 3: Graphical user interfaces and information visualisation.
Factor 4: Links and linking mechanisms.

Pathfinder networks can provide more accurate information about local structures than multidimensional scaling maps [13]. We found that the provision of explicit links in our maps made it easier to interpret interrelationships among different data points.

Furthermore, author co-citation maps provide a means of identifying research fronts, i.e. specialties in the field, and a visual aid of interpreting the results of factor analysis.