图是一种重要的数据模型，能够描述结构化的信息，在诸如交通网络、社交网络、Web页面链接关系等领域应用广泛，因而获得了广泛的研究. 海量的图数据管理对传统的图分析处理技术提出了挑战，分布式集群计算为大规模图数据分析提供了基础平台. 随着计算机硬件性价比的大幅提升以及高性能应用需求，基于内存计算的海量数据处理技术获得了业界青睐. 图数据高效存储和计算与内存计算密切相关，在此背景下，文章综述了大规模图数据处理相关技术进展，研究了典型的基于内存计算的大规模图数据管理系统，最后总结了基于内存计算的图数据管理的关键点.

Graph is an important data model, which can describe structural information including dependent relationship, such as transportation network, social network and webpage hyperlink. Management of big graph brings challenges for traditional techniques, however, distributed cluster provide platform and techniques for this problem. Nowadays, the ratio of performance and price of memory promote rapidly, while demand of applications of highperformance, inmemory computing for massive data management is becoming popular. The storage and evaluation of massive graph requires highperformance platform. In this context, it’s significant for studying graph data management with inmemory techniques. This paper surveyes key techniques of management of massive graph data, and researched graph data management of inmemory computing techniques，and finally summarizes the paper.