共生矩阵及其在情报学中的应用--将ACA扩展到Web环境
Co-occurrence Matrices and their Applications in Information Science: Extending ACA to the Web Environment
摘要：
共现矩阵，如共引矩阵、共词矩阵、共链矩阵等，在情报学中有着广泛的应用。然而，混乱和争议阻碍了对这些数据进行适当的统计分析。在我们看来，根本的问题涉及理解各种类型的矩阵的性质。本文讨论了对称共引矩阵和非对称共引矩阵之间的差异，以及可以分别应用于这些矩阵的适当的统计技术。相似性度量（如Pearson相关系数或余弦）不应应用于对称的共引矩阵，但可以应用于非对称的引文矩阵，以导出邻近矩阵。这一论点用实例加以说明。然后，这项研究扩展应用共生矩阵的Web环境中的可用数据的性质，因此数据汇集方法是不同于传统的数据库，如科学引文索引。利用传统的多变量分析方法和基于社会网络分析和图论的可视化软件Pajek对Google Scholar搜索引擎收集的一组数据进行了分析。
英文摘要：
Co-occurrence matrices, such as co-citation, co-word, and co-link matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of this data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This paper discusses the difference between a symmetrical co-citation matrix and an asymmetrical citation matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (like the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical co-citation matrix, but can be applied to the asymmetrical citation matrix to derive the proximity matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment where the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed using both the traditional methods of multivariate analysis and the new visualization software Pajek that is based on social network analysis and graph theory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
