Detecting communities or clusters in networks becomes a decisive issue in various interdisciplinary areas in recent years. Numerous methods are proposed to uncover community in networks, although the fundamental problem of most of the methods is the evolving nature of the networks and the presence of the imprecise number of communities. Since, real-world networks are scale-free networks and due to the preferentia attachment properties, the low degree nodes are attracted towards the hub nodes showing the power-law distributions. Hub nodes are highly surrounded by their neighbors and connectedness among the nodes within a community is larger than the others. As a result, the underlying structural details facilitate to uncover precise community structure. In this work, we present a multi-phase model ma-CODE to uncover communities based on the inherent association without having any prior information about the presence of the number of communities. The multi-phase approach contains the identification of high degree nodes, label propagation and community merging. The high degree nodes are identified based on the voting by the adjacent members; the label propagation is to assign the same community identification number to those members showing high similarity; the community merging is performed among the different communities only when there is a significant increase in the modularity after combination. We examine the competence of our proposed methods in the light of twelve (12) popular real-world social networks and eight (08) artificial networks. Experiments and simulation results using five (05) different statistical assessment parameters show that ma-CODE is superior over contemporary community detection methods.

近年来，在各个跨学科领域中，检测网络中的社区或集群已成为一个决定性的问题。尽管大多数方法的基本问题是网络的不断发展的性质以及存在数量不精确的社区，但仍提出了许多方法来揭示网络中的社区。由于现实世界的网络是无标度的网络，并且由于优先级的附加属性，低度节点被吸引到显示幂律分布的集线器节点。集线器节点被其邻居高度包围，并且社区内节点之间的连通性大于其他节点。结果，底层的结构细节有助于揭示精确的社区结构。在这项工作中，我们提出了一个多阶段模型ma-CODE在没有任何有关社区数量存在的先验信息的基础上，根据固有联系发现社区。多阶段方法包含高级节点的标识，标签传播和社区合并。基于相邻成员的投票来识别高级节点；标签传播是为那些具有高度相似性的成员分配相同的社区识别号；只有在合并后模块化显着增加时，才在不同社区之间执行社区合并。我们根据十二（12）个流行的现实世界社交网络和八（08）个人工网络检查了我们提出的方法的能力。使用五（05）个不同的统计评估参数进行的实验和仿真结果表明，ma-CODE优于当代社区检测方法。