The processing of graph data is restricted by large scale and complex structure. The graph summarization is to find a set of simple hyper-graphs or sparse graphs that clarify the main structural information or change trend of the original graph. According to the principle of minimum description length (MDL)

a summary model based on edit coding is proposed for attribute graphs

which unifies the similarity of structure and attribute as storage cost to construct editing behavior code. Based on this model

a greedy algorithm and a random algorithm are proposed

which store the editing information of attribute and structure

generate high-quality hyper-graphs

and support the reconstruction of original graphs. The experimental results show that the proposed model and algorithms in this paper have some advantages over other summarization algorithms in terms of compression rate and time cost.