To solve the problem that the current graph summarization methods have high compression ratios and the graph compression algorithms cannot be directly used in downstream tasks， a fusion algorithm of graph summarization and graph compression was proposed， which called Graph Summarization algorithm based on Node Similarity grouping and graph Compression （GSNSC）. Firstly， the nodes were initialized as super nodes， and the super nodes were grouped according to the similarity. Secondly， the super nodes of each group were merged until the specified number of times or nodes were reached. Thirdly， super edges and corrected edges were added between the super nodes for reconstructing the original graph. Finally， for the graph compression part， the cost of compressing and summarizing the adjacent edges of each super node were judged， and the less expensive one in these two was selected to execute. Experiments of graph compression ratio and graph query were conducted on six datasets such as Web-NotreDame， Web-Google and Web-Berkstan. Experimental results on six datasets show that， the proposed algorithm has the compression ratio reduced by at least 23 percentage points compared with SLUGGER （Scalable Lossless sUmmarization of Graphs with HiERarchy） algorithm， and the compression ratio decreased by at least 13 percentage points compared with SWeG （Summarization of Web-scale Graphs） algorithm. Experimental results on Web-NotreDame dataset show that the degree error of the proposed algorithm is reduced by 41.6% compared with that of SWeG algorithm. The above verifies that the proposed algorithm has better graph compression ratio and graph query accuracy.

Most traditional community detection methods are limited to single relational network， and their applicability and accuracy are relatively poor. In order to solve the problems， a community detection method for multiple relationship networks was proposed. Firstly， for modeling the multiple relational network， the third-order adjacency tensor was used， in which each slice of the tensor represented an adjacency matrix corresponding to a type of relationship between participants. From the perspective of data representation， by interpreting the multiple relational network as a third-order tensor is helpful to use the factorization method as a learning method. Then， RESCAL decomposition was used as a relational learning tool to reveal the unique implicit representation of participants. Finally， the evolutionary K-means clustering algorithm was applied to the results obtained in the previous step to determine the community structure in multiple dimensions. The experiments were conducted on a synthetic dataset and two public datasets. The experimental results show that， compared with Contextual Information-based Community Detection （CICD） method， Memetic method and Local Spectral Clustering （LSC） method， the proposed method has the purity at least 5 percentage points higher， the Overlapping Normalized Mutual Information （ONMI） at least 2 percentage points higher， and the F score at least 3 percentage points higher. And it is proved that the proposed method has fast convergence speed.