The main communication mode of graph computing applications is spatiotemporally random point-to-point fine-grained communication. However, existing high-performance computer network systems perform poorly when dealing with a large number of fine-grained communications, which affect the overall performance. The communication optimization in application layer can improve the performance of graph computing application effectively, but this brings great burden to application developers. Therefore, a structure-dynamic message aggregation technique was proposed and implemented, which produced a lot of intermediate points in the communication path by building virtual topologies, so as to greatly improve the effect of message aggregation. By contrast, the traditional message aggregation strategy generally performed only at the communication source or destination with limited aggregation chances. In addition, this proposed technique adapted different kinds of hardware conditions and application features by flexibly adjusting the structure and configuration of the virtual topology. At the same time, the runtime system with message aggregation for graph computing was proposed and implemented, which allowed the runtime system to dynamically select parameters when executing iterations, so as to reduce the burden of developers. Experimental results on a system with 256 nodes show that typical graph computing application performance can achieve more than 100% improvement after optimized by the proposed message aggregation technique.