The community evolution relationships extracted by the traditional adjacent time slice analysis cannot fully describe the entire community evolution process in dynamic graphs. Therefore, an improved community evolution relationship analysis method was proposed. First, the community events were defined, and the evolution states of the community were described according to the occurred community events. Then, the event matching was performed on two communities within different time slices to obtain community evolution relationships. Results of comparison with the traditional methods show that the total number of community events detected by the proposed method is more than twice that revealed by the traditional method, which proves that the proposed method can provide more useful information for describing the evolution process of communities in dynamic graphs.

Focusing on how the large-scale graphs' structural features affect the partitioning quality, through the structural features of vertex degree, a method of describing large-scale graphs' structural features was proposed. Firstly, based on the real graph data, a several simulation datasets with the same numbers of vertices and edges but different structural features were generated. Through the similarity between the real graph and the simulation graph calculated by the proposed algorithm, the validity of the method for describing the structural features of the real large-scale graphs was verified. Secondly, the relationship between the structural features of the graph and the quality of partitioning was verified by the Hash algorithm and point-to-point exchange algorithm. When the point-to-point algorithm was performed for 50000 times, the number of crossed edges of a real graph with 6301 vertexes and 20777 edges was reduced by 54.32% compared with the Hash partitioning algorithm. When the two graphs with entirely different structural features in the simulation data were partitioned, the number of crossed edges were 6233 and 316 respectively. The experimental results show that the point-to-point algorithm can reduce the number of crossed edges. The larger the difference of the vertex degree distribution and the smaller the number of crossed edges are, the better the partitioning quality is. Therefore, the structural features of large graphs affect the partitioning effect, which lays the foundation for the model investigation of the relationship between structural features and partitioning quality.