To deal with shortcomings of a single node or the simple neighbor relations in the research of cascading failures, a cascading failure model was proposed considering the local characteristics of node-community structure. The model gave each node dynamic initial load value based on the community property of the node, and adopted different strategies to attack the Western States Power Grid of the United States, US Air lines, IEEE118 standard grid and ScaleF-ree Network (SFN) to simulate the process of cascading failures. The simulation results show that these nodes within community lead to relative minor faults when community factor dominated in initial load, but some special nodes connecting multiple communities will cause serious cascading failures. It also indicates that the property of the number of neighbor nodes is more relevant than other properties by calculating Pearson correlation coefficients of different properties.

Traditional spreading models have difficulties in descripting the complex activity patterns and the topological differences between nodes in online social networks, and the contact-based spreader annihilation mechanisms in these models do not fit with the reality. To filling the gap between spreading simulations of theoretical model and realities of information spreading, a new dynamic information spreading model (D-SIR) based on online social network was proposed. With consideration of some practical factors in information dissemination process, this model introduced the time delay annihilation mechanism that spreaders changed to stiflers spontaneously and the dynamic authority and resistance of nodes mechanism to apply to inhomogeneous networks, and considered the receiving reinforced signal effect and the social reinforcement. With the variances of parameters, the simulations on the real-world online social network which is constructed by crawled Sina microblog data verify that D-SIR model can reflect the real spreading situation in online social network. And compared to the traditional spreading model, the new model is more flexible and extensible.