Calculation of the information network data cube (InfoNetCube) is the foundation of information online analytical processing. However, different from the traditional data cube, InfoNetCube consists of multiple lattices in which each cuboid contains a topic graph (or graph measurement), thus the storage consumption overhead is two orders of magnitude more than that of traditional data cube. How to materialize the specified cuboids or lattice rapidly and efficiently in the information network is a quite challenging research issue. In this paper, a novel InfoNetCube materializing strategy for information network is proposed based on dialysis computing. By leveraging the anti-monotonicity of topic graph measurement in the information and topology dimensions, a dialysis based space pruning algorithm is constructed to rapidly dialysis out the hidden sub graph, cuboids and lattices. Experimental results show that the proposed partial materialization algorithm outperform the cube based partial materialization strategy, saving almost 75% aggregation time.