  With the advent of the big data, graph are processed in an iterative manner,
which incrementally described in the form of graph in big data applications.
Most currently, graph processing methods treat the underlying map data as black
boxes. However, as shown in experimental evaluation, graph structures often
have diversity, different graph processing methods are very sensitive to the
graph structure and show different performance for different data sets. Based
on this, a graph processing method for graph structure analysis is proposed in
this paper: (1) This paper calculates the vertex activity of a graph according
to the in-degree and out-degree, and divide the corresponding vertices into the
hot or cold partitions; (2) According to the change of graph structure caused
by partial vertex convergence after iteration, this paper reclassifies the
partitions, divides the lower active vertices into cold partition and reduces
the frequency of calculation, which thereby reducing the cache miss rate and
the I/O overhead caused by active vertices as well; (3) The partition with
highest vertex status degree are given a priority calculation in this paper. In
detail, more pronounced and more frequent vertices have higher processing
priority. In this way, the convergence speed of the graph vertices is
accelerated, and the running time of the graph algorithm in the big data
environment is reduced. Our experiments show that compared with the latest
system, the proposed method can double the performance of different graph
algorithms and data sets.
