The community structure plays an indispensable role in developing the deep structure of complex networks. In recent years, some researchers have realized the importance of leader nodes in the community detection process. However, most of the existing leader-based algorithms only use the topological information of networks or attribute information to supplement the topological information, resulting in a significant loss of information integrity. In this paper, we propose a leader-based method that combines topological and attribute information (TALB), uses attribute information among nodes in the network to construct an attribute similarity matrix, and then combines it with network topological information to establish dependency relationships among nodes. As a result, a dependency tree is formed, and the final result of the community division is obtained. Experiments on synthetic networks and real networks show that our proposed method is more effective and practical than the existing leader-based algorithms.