一句话：提出基于编译与模式分解的图模式挖掘系统 DecoMine，自动选择分解算法并降低编程开销。
图模式挖掘（GPM）用于从图中识别结构，但现有系统与高效的模式分解算法之间仍有至少一个数量级的性能差距。本文解决将模式分解引入 GPM 系统的两个根本障碍：一是分解方式选择困难，作者构建带通用与 GPM 专用优化的编译器，为不同分解选择生成算法，并用精确代价模型评估选优；二是设计保持分解算法优势的新型 partial-embedding API。基于此实现的 DecoMine 在大型图和模式上把执行时间从数天缩短到数小时，同时降低编程开销。
Graph pattern mining (GPM) is an important application that identifies structures from graphs. Despite the recent progress, the performance gap between the state-of-the-art GPM systems and an efficient algorithm—pattern decomposition—is still at least an order of magnitude. This paper clears the fundamental obstacles of adopting pattern decomposition to a GPM system. First, the performance of pattern decomposition algorithms depends on how to decompose the whole pattern into subpatterns. The original method performs complexity analysis of algorithms for different choices, and selects the one with the lowest complexity upper bound. Clearly, this approach is not feasible for average or even expert users. To solve this problem, we develop a GPM compiler with conventional and GPM-specific optimizations to generate algorithms for different decomposition choices, which are evaluated based on an accurate cost model. The executable of the GPM task is obtained from the algorithm with the best performance. Second, we propose a novel partial-embedding API that is sufficient to construct advanced GPM applications while preserving pattern decomposition algorithm advantages. Compared to state-of-the-art systems, our new GPM system, DecoMine, developed based on the ideas, reduces the execution time of GPM on large graphs and patterns from days to a few hours with low programming effort.