مشخصات پژوهش

Community structure is vital to discover the important structures and potential properties of complex networks. In the recent years, the increasing quality of local community detection approaches has become a hot spot in the study of complex networks due to the advantages of linear time complexity and applicable for large-scale networks. However, there are many shortcomings in these methods such as instability, low accuracy, and randomness. The G-CN algorithm is one of the local methods that uses the same label propagation as the LPA method but unlike LPA, only the labels of boundary nodes are updated at each iteration that reduces its execution time. However, it has a resolution limit and a low accuracy problem. In order to overcome these problems, this paper proposes an improved community detection method called SD-GCN, which uses a hybrid node scoring and synchronous label updating of boundary nodes along with disabling random label updating in initial updates. In the first phase, it updates the label of boundary nodes in a synchronous manner using the obtained score based on the degree centrality and common neighbor measures. In addition, we define a new method for merging communities in a second phase, which is faster than the modularity-based methods. Extensive set of experiments are conducted to evaluate the performance of SD-GCN on small- and large-scale real-world networks and artificial networks. These experiments verify a significant improvement in the accuracy and stability of community detection approaches in parallel with a shorter execution time in a linear time complexity.