A semi-supervised GCN-based community detection algorithm

Community detection reveals the unique characteristics and relationships of in-network members, differentiated from out-of-community members and plays a pivotal role in network analysis. In recent years, deep learning techniques have made great strides in the application of community detection, especially on label sampling models, which train graph convolutional networks by constructing a balanced training set through structural centre localization and neighbourhood node expansion. However, such algorithms are limited on centre selection in community structure. To address this, this paper introduces a novel community detection algorithm based on peak density adaptive iterative segmentation, or LDACN in short. First, labels are assigned by adaptively selecting the centre node of the community structure, which results in a more even distribution of labels in the network. Subsequently, more accurate community segmentation is achieved by taking advantage of GCN's combined ability in capturing the connectivity relationships between the nodes and their intrinsic characteristics. Experimental results on synthetic and real-world network datasets show that our algorithm improves the effectiveness of community segmentation compared to the state-of-the-art algorithms.