真值？基于概念的社区与物理学手稿的外部分类
Ground truth? Concept-based communities versus the external classification of physics manuscripts
摘要：
社区发现技术被广泛用于推断互联系统中的隐藏结构。尽管在基准测试中表现出很高的准确性，但它们再现了许多现实世界系统的外部分类，并存在显著的差异。这种结果背后的一个被广泛接受的原因是当原始复杂系统被表示为网络时，遇到的非拓扑信息（如节点属性）的不可避免的丢失。在这篇文章中，我们强调，观察到的差异也可能是由不同的原因造成的：外部分类本身。为此，我们使用科学出版物数据，i）展示了一个定义良好的模块化结构，ii）持有专家制定的研究文章分类。在将文章和提取的科学概念表示为二分网络及其单分投影之后，我们应用模块化优化来揭示内部主题结构。所得到的集群显示，部分反映了作者作出的分类，虽然观察到一些显着的差异。对这些差异的详细分析表明，它们携带了关于系统的基本信息，主要涉及在不同（子）学科中使用类似的技术和方法，否则，当只考虑外部分类时，这些信息就会被忽略。
英文摘要：
Community detection techniques are widely used to infer hidden structures within interconnected systems. Despite demonstrating high accuracy on benchmarks, they reproduce the external classification for many real-world systems with a significant level of discrepancy. A widely accepted reason behind such outcome is the unavoidable loss of non-topological information (such as node attributes) encountered when the original complex system is represented as a network. In this article we emphasize that the observed discrepancies may also be caused by a different reason: the external classification itself. For this end we use scientific publication data which i) exhibit a well defined modular structure and ii) hold an expert-made classification of research articles. Having represented the articles and the extracted scientific concepts both as a bipartite network and as its unipartite projection, we applied modularity optimization to uncover the inner thematic structure. The resulting clusters are shown to partly reflect the author-made classification, although some significant discrepancies are observed. A detailed analysis of these discrepancies shows that they carry essential information about the system, mainly related to the use of similar techniques and methods across different (sub)disciplines, that is otherwise omitted when only the external classification is considered.
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。
