Heterogeneous information networks contain heterogeneous types of nodes and edges, e.g., social networks and knowledge graphs. A meta-path is a path connecting nodes through a sequence of heterogeneous edges, representing different kinds of semantic relations among nodes. Meta-paths are good mechanisms to improve the quality of graph analysis on heterogeneous information networks. This paper presents an iceberg cube framework for heterogeneous information networks based on meta-paths. To the best of our knowledge, there is no such proposal in the past. (1) We use meta-paths to measure the similarities of nodes, and prove the problem is NP-hard. (2) An optimal solution is proposed for the strict case. We develop the variant of slice tree to aggregate networks hierarchically. (3) To improve the scalability, a general approximate algorithm is provided for fast aggregation, where random walk on meta-paths is employed to measure the similarities. (4) Two pruning strategies are designed for reducing search space when the aggregate function is monotonic. (5) Experiments on both real-world and synthetic networks demonstrate the effectiveness and efficiency of the algorithms.