Community Detection Based on a Preferential Decision Model The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a nontrivial task due to the difficulty of setting specific parameters, high randomness and lack of groundtruth information. In this paper, we explore a new decisionmaking method through reallife communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in realworld networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

News Recommendation Model Based on Improved Label Propagation Algorithm Facing the everexpanding scale of news information, how to filter and redundant information in complex and diverse data to accurately recommend information to users has become an important challenge. News recommendations have become a powerful tool for dealing with information overload. Scholars have done a lot of research on personalized news recommendation. However, due to the sparseness of user data, the traditional collaborative filtering algorithm is not only too timeconsuming but also less accurate. Therefore, aiming at the characteristics of academic social networks, we propose a news recommendation model based on community detection, which builds a friend relationship community by using the users friend relationship and coherent neighborhood propinquity algorithm, and then integrates the collaborative filtering algorithm to recommend news to users. Through verification on the dataset of the academic social network SCHOLAT, we can prove that the recommendation model can achieve good accuracy while improving recommendation efficiency.