In this work we give a community detection algorithm in which the communities both respects the intrinsic order of a directed acyclic graph and also finds similar nodes. We take inspiration from classic similarity measures of bibliometrics, used to assess how similar two publications are, based on their relative citation patterns. We study the algorithm’s performance and antichain properties in artificial models and in real networks, such as citation graphs and food webs. We show how well this partitioning algorithm distinguishes and groups together nodes of the same origin (in a citation network, the origin is a topic or a research field). We make the comparison between our partitioning algorithm and standard hierarchical layering tools as well as community detection methods. We show that our algorithm produces different communities from standard layering algorithms.

在这项工作中，我们给出了一种社区检测算法，其中社区既尊重有向无环图的内在顺序，又找到相似的节点。我们从文献计量学的经典相似性度量中汲取了灵感，这些度量用于根据两个文献的相对引用模式来评估两个出版物的相似性。我们在人工模型和真实网络（例如引文图和食物网）中研究算法的性能和反链特性。我们展示了这种划分算法如何很好地将相同来源的节点区分和分组（在引用网络中，来源是一个主题或研究领域）。我们对分区算法和标准分层分层工具以及社区检测方法进行了比较。