Community detection based on competitive walking network embedding method

Currently, much of the information of the real world is network-structured, and extracting hidden information from network-structured data helps to understand the corresponding systems, but can also be a challenging problem. In recent years, network embedding has been an effective way to extract network information, which represents nodes in complex networks as low-dimensional space vectors, while preserving the properties of the network. Community attributes are an important property of networks, and in most network embedding algorithms, the community structure is usually ignored or cannot be explicitly preserved. In this paper, we propose a new network embedding framework that explicitly considers community structure feature extraction. The framework, called competitive walking network embedding (CWNE), extracts sample sequences by competitive walking and obtains node representation vectors by skip-gram training. Competitive walking allows the extracted sample sequences to be concentrated within the same community, effectively preserving the community structure features of the network. The results of testing the proposed method on artificial and real-world networks show that our model is more effective in detecting community structure in networks. In addition, visualization experiments show that the results of CWNE show that nodes from the same community are more tightly distributed in low-dimensional space.