Community identification refers to the process of selecting dense clusters with sparse connections to the rest of the graph. These communities naturally overlap in many social networks, while each node can participate in several communities. Revealing these structures represents an important task in network analysis. In fact, it allows to understand the features of networks. However, many community detection approaches fail to provide communities with high quality and better ground truth correspondence in reasonable execution time. The key idea of this paper is to propose a coupled-seed expansion method for the overlapping community detection. Specifically, we construct a coupled-seed by choosing a node and its most similar neighbor and then expand this coupled-seed using a fitness function that improves the identification of local communities. The overlapping modularity, the F-score and the extended normalized mutual information measures are used to evaluate the proposed algorithm. The experimental results on 10 instances of real networks and four sets of LFR networks prove that the proposed method is effective and outperforms the existing algorithms (BigClam, OSLOM, SE, Demon, UMSTMO, LC, Ego-Splitting).

社区识别是指选择与图的其余部分稀疏连接的密集群集的过程。这些社区自然会在许多社交网络中重叠，而每个节点都可以参与多个社区。揭示这些结构是网络分析中的一项重要任务。实际上，它允许了解网络的功能。但是，许多社区检测方法无法在合理的执行时间内为社区提供高质量和更好的地面事实对应。本文的关键思想是提出一种用于重叠社区检测的耦合种子扩展方法。具体来说，我们通过选择一个节点及其最相似的邻居来构建配对种子，然后使用适合度函数扩展该配对种子，该功能可以改善对本地社区的识别。重叠的模块性，F分数和扩展的归一化互信息度量用于评估所提出的算法。在10个真实网络实例和4组LFR网络实例上的实验结果证明，该方法是有效的，并且优于现有算法（BigClam，OSLOM，SE，Demon，UMSTMO，LC，自我拆分）。