Towards Graph Summary and Aggregation: A Survey

To obtain the insight in a single large graph and to save the space consumption for graph mining, the graph summary transforms the input graph into an aggregated concise super-graph represented by supernodes and superedges. In this paper, we investigate current algorithms of the graph summary and aggregation. We provide the classification of them in terms of partition criterion or information lossless. Further, the main graph summary algorithms are compared and discussed in detail. In the end, we give the challenges and future works.