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With the rapidly growing demand of graph analytics, a large number of iterative graph processing jobs are often executed concurrently on the same platform.However, the existing graph processing system is mainly designed to efficiently perform a single graph processing job. Therefore when concurrent jobs are executed on the same underlying graph the system will suffer from high data access cost.To improve the throughput of the concurrent jobs, existing out-of-core concurrent graph processing schemes reduce the data storage and access cost by sharing graph data among these jobs.Due to the power-law property of real-world graphs and the differentiation between the concurrent jobs, existing schemes still suffer from a large amount of unnecessary I/O traffic, because even if the most proportion of the vertices in the static graph partition is inactive or only shared by a few jobs, this partition will still be entirely …