TSMH Graph Cube: A novel framework for large scale multi-dimensional network analysis

As a representation of information, Multi-dimension network is more and more popular, such as web data and social network. With the increment of data source, the entities of the network become diverse. How to analyze these multi-dimensional heterogeneous networks effectively and efficiently is a big challenge. In this paper, we propose a Two-Step Multi-dimensional Heterogeneous (TSMH) Graph Cube framework. We use the meta path in heterogeneous network to guide the aggregation of the network and build the Entity Hyper Cube. For the cuboid in Entity Hyper Cube, we do dimension roll-up/drill down to build the Dimension Cube. In Entity Hyper Cube, we design meta path aggregation algorithms and propose materialization strategy. In Dimension Cube, we use hierarchical coding for entities and dimensions and it saves the process of join operations of entities and dimensions which greatly improve the efficiency of dimension operations. In addition, we propose more new Graph OLAP operations which can make network analysis more diverse. At last, we implement the framework in Spark. The results of experiments on real data set and synthetic data set confirm the efficiency and effectiveness of our framework.