Research on Social Network Inference Method Based on ConNIe Algorithm In recent years, Internet technology and online social networks have developed rapidly, enabling people to express their opinions, ideas, emotional exchanges and economic exchanges randomly. Inferences about social networks are made possible by observational data exchanged by people on the Internet. In this paper, through the analysis of ConNIe algorithm, the effects of sparse parameter, propagation time distribution model and its parameters on the inferred results of this algorithm are studied. Then, based on the research, this paper use perceptron algorithm to classify the propagation time distribution model and use particle swarm optimization algorithm to optimize the sparse parameter and the parameters of propagation time distribution model. Finally, a social network inference framework based on ConNIe algorithm is proposed to make up for ConNIe. Some of the shortcomings of the algorithm have gotten over. The research in this paper helps people to understand the social network itself, and it has a wide range of practical value in the fields of social public opinion control and marketing.

A CoEvolutionary Model for Inferring Online Social Network User Behaviors Accurate online social network user behavior inference can improve the performance of the other applications significantly, such as friend commendation, hot topic prediction, and personal website assistant. Previous works mainly focus on the trend analysis of user behaviors or adopt the method to fit the supposed userbehavior distribution, but they ignore the dynamic mutual influence among the users and behaviors on social networks. This paper proposes a coevolutionary model to formulate the interaction pattern among the users and behaviors, in which a systematic method is used for embedding the distinctiveness and permanence properties of the users and behaviors into latent features. This model could naturally capture the dynamic evolving process of the user behaviors with the time. Whatxe2x80x99s more, we also take into account the following relationship to depict the interaction information among users. Extensive experiments show that our algorithm achieves 0.024 of the MAE (hour) in the crime time inference, and 0.506 and 0.579 of the accuracy in the user and behavior inference, which surpass the stateofarts more than 7.19xc3x97, 1.12xc3x97 and 1.14xc3x97, respectively. Additional experiments on different training echoes of our model are provided to further explore its effectiveness and scalability.