With the rapid development of network information technology, data is gradually developing towards multi-source heterogeneity. Inside the multi-source heterogeneous data lies countless inextricable relations. And this kind of relations, together with the network structure, are surely essential for data analysis. Unfortunately, when it comes to large scale data analysis, the traditional relational databases are poor in association detection and expressions. Therefore, graph data has attracted great attention for its powerful ability in expressions. Graph computing uses a graph model to express and solve the problem. Graphs can integrate with multi-source data types.
In addition to displaying the static basic features for data, graph computing also finds its chance to display the graph structure and relationships hidden in the data. Thus graph becomes an important analysis tool in social network, recommendation system, knowledge graph, financial risk control, network security, and text retrieval.
