Aiming at the problems of premature convergence and low recall caused by using greedy strategy for community expansion in the existing local community detection algorithms， a local community detection algorithm based on Monte-Carlo iterative solving strategy was proposed. Firstly， in the community expansion stage of each iteration， the selection probabilities were given to all adjacent candidate nodes according to the contribution ratio of each node to the community tightness gain， and one node was randomly selected to join the community according to these probabilities. Then， in order to avoid random selection causing the expansion direction to deviate from the target community， it was determined whether the node elimination mechanism was triggered in this round of iteration according to the changes in community quality. If it was triggered， the similarity sum of each node joining the community and other nodes in the community was calculated， the elimination probabilities were assigned according to the reciprocal of the similarity sum， a node was randomly eliminated according to these probabilities. Finally， whether to continue the iteration was judged on the basis of whether the community size increased in a given number of recent iteration rounds. Experimental results show that， on three real network datasets， compared to Local Tightness Expansion （LTE） algorithm， Clauset algorithm， Common Neighbors with Weighted Neighbor Nodes （CNWNN） algorithm and Fuzzy Similarity Relation （FSR） algorithm， the proposed algorithm has the F-score value of local community detection results increased by 32.75 percentage points， 17.31 percentage points， 20.66 percentage points and 25.51 percentage points respectively， and can effectively avoid the influence of the location of the query node in the community on the local community detection results.

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.

Focusing on the problem of Virtual Machine Placement (VMP) in heterogeneous cloud environment, a Resource Demand Distribution Feature based Placement Algorithm (RDDFPA) for virtual machines was proposed. Firstly, a method of describing virtual machine requirements and physical machine configuration based on scale factor of CPU resource and memory resource was established. Based on the scale factor, all the virtual machines were sorted. Secondly, by analyzing the proportion relationship of virtual machine requirements and physical machine configuration in the CPU resources and memory resources, the proportion demarcation point was determined, and the partition of virtual machine set was completed. The requirement proportion of matched physical machines with different configurations was reflected by the size of each virtual machine subset.Finally, by using the heuristic algorithm such as the First Fit algorithm, the virtual machine subset was placed on the subset of physical machines with matched configuration. Theoretical analysis and simulation experimental results show that, compared with the total number of physical machines with any single configuration, the total number of physical machines required by the proposed algorithm is reduced by 2%-17%.The proposed RDDFPA can determine the number of physical machines with various configurations according to the distribution of virtual machine resource requirements, and efficiently complete the placement of virtual machines, which can improve the resource utilization rate and reduce the system energy consumption.