论文标题
在引文图中找到科学社区:收敛聚类
Finding Scientific Communities In Citation Graphs: Convergent Clustering
论文作者
论文摘要
了解科学社区的性质和组织是广泛的关注。 “看不见的学院”是一个这样一个社区的历史隐喻,可以寻找这样的“大学”,因为对研究共同利益问题的一小部分科学家进行了检测和分析。案例研究以前是针对各个社区的科学和社会行为进行的。在这项研究中,我们介绍了一种新的可扩展社区发现方法。通过专家评估,我们使用两种不同的聚类方法的融合来选择来自超过200万篇免疫学领域的文章群集,这些文章跨越了11年,并具有相关的集群质量指标,以进行评估。最后,我们确定了这些集群定义的作者社区。专家审查了该管道生产的文章集群的样本,并显示出强烈的主题相关性,这表明推断的作者社区可能代表有效的实践社区。这些发现表明,这种收敛的方法在将来可能很有用。
Understanding the nature and organization of scientific communities is of broad interest. The `Invisible College' is a historical metaphor for one such type of community and the search for such `colleges' can be framed as the detection and analysis of small groups of scientists working on problems of common interests. Case studies have previously been conducted on individual communities with respect to their scientific and social behavior. In this study, we introduce, a new and scalable community finding approach. Supplemented by expert assessment, we use the convergence of two different clustering methods to select article clusters generated from over two million articles from the field of immunology spanning an eleven year period with relevant cluster quality indicators for evaluation. Finally, we identify author communities defined by these clusters. A sample of the article clusters produced by this pipeline was reviewed by experts, and shows strong thematic relatedness, suggesting that the inferred author communities may represent valid communities of practice. These findings suggest that such convergent approaches may be useful in the future.