Graph aggregation is a technology for representing a large scale graph with a concise graph that can preserve the structural and attribute information of the original large graph. Existing algorithms consider either the attribute information of nodes or the weight information of edges, and the difference between the original graph and the aggregated graph can thus be huge. So we propose a graph aggregation method considering both the attribute information of nodes and the weight information of edges, which enables the aggregated graph not only to preserve the similarity of node attributes but also edge weight information. Firstly, we define the closed neighborhood structural similarity, and use a structure pruning strategy to calculate the structural similarity between nodes. Secondly, minimum hash (Minhash) technique is employed to calculate the attribute similarity between nodes, and the proportions of structure similarity and attribute similarity are adjusted, based on which the weighted graph is aggregated. Experiments prove the feasibility and effectiveness of our method.