Most data in real life can be described as multidimensional networks. How to process the analysis on multidimensional networks from multiple views and multiple granularities is still the focus of current research. Meanwhile, OLAP (online analytical processing) technology has been proven to be an effective tool on relational data. However, it is an enormous challenge to manage and analyze multidimensional heterogeneous networks via OLAP technology to support effective decision making. In this paper, a P&D (path and dimension) graph cube model is proposed. Based on this model, the graph cube materialization is divided into two parts, termed as path related materialization and dimension related materialization, and the corresponding materialization algorithms are designed. Some GraphOLAP operations are also refined to improve the ability of analyzing multidimensional networks. Finally, the algorithms are implemented on Spark and the multidimensional networks are constructed through real datasets. These networks are then analyzed using the framework. The results of experiments validate the effectiveness and scalability of P&D graph cube model and the materialization algorithms.