An improved label propagation algorithm based on community core node and label importance for community detection in sparse network

Community structure can be used to analyze and understand the structural functions in a network, reveal its implicit information, and predict its dynamic development pattern. Existing community detection algorithms are very sensitive to the sparsity of network, and they have difficulty in obtaining stable community detection results. To address these shortcomings, an improved label propagation algorithm combining community core nodes and label importance is proposed (CCLI-LPA). Firstly, the core nodes in a network are selected by fusing the first-order and second-order structures of the nodes, and the network is initialized by them. Then, a new label selection mechanism is defined by combining the importance of both neighboring nodes and their labels, and the label of a node is updated based on it. Validation experiments are conducted on six real networks and eight synthetic networks, and the results show that CCLI-LPA can not only obtain stable results in real networks but also obtain stable and accurate results in sparse networks.