According to the research into how the users social characteristics and behavior properties influence the microblog fans network evolving, a new microblog fans network evolving model based on the attractive factor called SBPAF was proposed. The attractive factor for social characteristics and the attractive factor for behavior properties were defined. The nodes create new edges according to attractive factor preferential attachment principle and the two-step attachment principle. Besides, the dying out of the edges was also considered. The parameter in the model can be adjusted flexibly so that different microblog fans networks can be simulated. Finally, the mathematical analysis and computational experiments verify that SBPAF model is reasonable and available.

In recent years, with the advances in information communication and organizational ability, microblog has attracted the attention of scholars of all kinds. This paper reviewed the present study of microblog based on the information data analysis, and presented the concept of three components in microblog information transmission. Besides it summarized the main problems and methods in this field, generalized the domestic and foreign achievement. Finally, the trend for future work on the monitoring and management of microblog was discussed.

Combined Particle Swarm Optimization (PSO) and Solver add-in, this paper proposed a hybrid PSO-Solver algorithm to solve the optimization problems. As a global search algorithm, PSO looks for the global feasible solution, and Solver is a local search tool based on gradient information, which refines the solution obtained by PSO. The hybrid algorithm could speed up the global search, as well as avoid getting into local minima. VBA was used to code, which is simple and easily conducted. Results of solving some unconstrained and constrained examples, compared to the standard PSO and other heuristic algorithms, show that this hybrid PSO-Solver algorithm can improve the speed of convergence and the accuracy of solutions significantly.