Preference Memor Based Social Network Model Research

The main topic of this thesis content is social network modeling, its analysis and optimization. Through the existing literature in complex networks and social networks we find new progress on random networks, small-world networks and scale-free networks, especially in recent years more popular field of study-Social Network and we found that there is a common deficiency in detailed analysis and simulation with these models, the network models do not take into account the heterogeneity of nodes and memory on node connections preference. In response to these problems, this paper proposes a dynamic network, not only has the small-world and scale topological properties, and can have node heterogeneity and memory into the network model, to solve the existing social network modeling difficult in order to reflect real-world network situations, In this paper, analyzing topological features and statistical characteristics on the new social network model, we found not only the network model have the characteristic like small-world, scale-free, high clustering coefficient and short average shortest path, but also the network model has a certain community structure with analysis on modularity, which provides a solid foundation for further optimization on network model and study model dynamics behavior.