Graph mining is an emerging application of great importance to big data analytic. Graph mining algorithms are bottle-necked by both computation complexity and memory access, hence necessitating specialized hardware accelerators to improve the processing efficiency. Current accelerators have extensively exploited task-level and fine-grained parallelism in these algorithms. However, their task scheduling still has room for optimization. They use either breadth-first search, depth-first search or a combination of both, leading to either poor intermediate data locality, low parallelism or inter-depth barriers.