Community detection in directed networks appears as one of the most relevant topics in the field of network analysis. One of the common themes in its formalizations is information flow clustering in a network. Such clusters can be extracted by using triads, expected to play an important role in the detection of that type of communities since communities could be centered round core nodes called kernels. Triads in directed graphs are directed sub-graphs of three nodes involving at least two links between them. To identify communities in directed networks, this paper proposes an in-seed-centric scheme based on directed triads. We also propose a new metric of the communities' quality based on the triad density of communities. To validate our approach, an experiment was conducted on some networks showing it has better performance on triad-based density over some state-of-the-art methods.

中文翻译：

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使用三元组的有向网络中的社区结构提取

有向网络中的社区检测似乎是网络分析领域中最相关的主题之一。其形式化的共同主题之一是网络中的信息流聚类。可以通过使用三元组来提取此类集群，因为社区可以以称为内核的核心节点为中心，因此有望在检测此类社区中发挥重要作用。有向图中的三元组是三个节点的有向子图，它们之间至少涉及两个链接。为了识别有向网络中的社区，本文提出了一种基于有向三元组的以种子为中心的方案。我们还根据社区的三元组密度提出了一个新的社区质量指标。为了验证我们的方法，