Community detection can exhibit the aggregation behavior of complex networks. Network motifs are the fundamental building blocks which can reveal the higher-order structure of complex networks. Label propagation algorithm has the advantage of approximately linear time complexity, unfortunately, the randomness of label update is a major but unsolved issue. For these reasons, this paper proposes a novel community detection method, named motif-based embedding label propagation algorithm (MELPA). First, complex network topology is reconstructed by merging higher-order topology with lower-order connectivity features, where higher-order topology is captured by mining network motifs. Second, We design a label propagation characteristic model according to nodes influence, then a new label update rule is formulated based on reconstructed weighted network, the rule integrates frequency among neighbor labels, influence of nodes, propagation characteristics and closeness of nodes to update the node label, the purpose is to overcome the randomness of label selection and identify a better and more stable community structure. Finally, extensive experiments on synthetic networks and real-world complex networks are conducted to verify the effectiveness of MELPA, especially for the complex networks with unobvious community structure, MELPA will get unexpected results.

中文翻译：

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社区检测中基于Motif的嵌入标签传播算法

社区检测可以表现出复杂网络的聚合行为。网络基序是揭示复杂网络高阶结构的基本组成部分。标签传播算法具有近似线性时间复杂度的优点，不幸的是，标签更新的随机性是一个主要但尚未解决的问题。出于这些原因，本文提出了一种新颖的社区检测方法，称为基于主题的嵌入标签传播算法（MELPA）。首先，通过将高阶拓扑与低阶连通性特征合并来重构复杂的网络拓扑，其中高阶拓扑通过挖掘网络主题来捕获。其次，我们根据节点影响设计标签传播特征模型，然后基于重构加权网络制定新的标签更新规则，该规则综合邻居标签之间的频率、节点的影响、传播特性和节点的接近度来更新节点标签，目的是克服标签选择的随机性，识别出更好更稳定的社区结构。最后，在合成网络和现实世界的复杂网络上进行了大量的实验，以验证 MELPA 的有效性，特别是对于社区结构不明显的复杂网络，MELPA 会得到意想不到的结果。