Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X‐means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X‐means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real‐attributed and synthetic‐attributed networks show that the performance of the proposed algorithm is excellent with multiple evaluation metrics.

中文翻译：

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LPX：属性网络中基于X均值和标签传播算法的重叠社区检测

属性网络（例如，社交网络）中的传统社区检测方法通常会忽略大量的节点属性信息，而只关注图的结构信息。属性网络中现有的社区检测方法主要应用于非重叠社区的检测，不能直接用于检测重叠结构。本文提出了一种属性网络中的重叠社区检测算法。首先，我们使用改进的X均值算法对属性进行聚类以形成不同的主题。其次，我们采用基于邻居网络电导的优先级和主题权重规则的标签传播算法（LPA）来检测每个主题中的社区。最后，我们执行冗余处理以形成最终的社区划分。所提出的算法改进了X均值算法，从而避免了异常值的影响。LPA的问题，例如分区的不稳定性和邻近社区容易合并，可以通过优先考虑节点邻域网络电导来解决。当在属性子空间中检测到社区时，该算法可以找到重叠的社区。在真实属性和合成属性网络上的实验结果表明，该算法在多种评估指标下均具有出色的性能。该算法可以找到重叠的社区。在真实属性和合成属性网络上的实验结果表明，该算法在多种评估指标下均具有出色的性能。该算法可以找到重叠的社区。在真实属性和合成属性网络上的实验结果表明，该算法在多种评估指标下均具有出色的性能。