arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.14452cs.DLcs.SIphysics.soc-ph
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在引文图中发现科学社区:收敛聚类

Finding Scientific Communities In Citation Graphs: Convergent Clustering

Shreya Chandrasekharan, Mariam Zaka, Stephen Gallo, Tandy Warnow, George Chacko

摘要:

了解科学界的性质和组织具有广泛的意义。“无形学院”是对此类社区的一种历史隐喻,寻找此类“学院”的工作可以被界定为发现和分析研究共同感兴趣问题的科学家小组。案例研究以前已经进行了个别社区就其科学和社会行为。在这项研究中,我们介绍,一个新的和可扩展的社区发现的方法。在专家评估的辅助下,我们使用两种不同聚类方法的收敛性来选择来自免疫学领域的200多万篇文章的文章聚类,这些文章跨越了11年的时间,具有相关的聚类质量指标进行评估。最后,我们确定了这些集群定义的作者社区。专家审查了这一管道产生的文章组样本,显示出很强的主题相关性,表明推断的作者群体可能代表有效的实践群体。这些研究结果表明,这种趋同的方法在未来可能是有用的。

英文摘要:

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.

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。

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