GraphMini：利用辅助图加速图模式匹配
GraphMini: Accelerating Graph Pattern Matching Using Auxiliary Graphs
AI总结：
本文提出 GraphMini 系统，通过查询编译与成本模型在执行期构建并维护辅助图，主动剪枝以加速图模式匹配，在常用基准上较最先进子图枚举系统提速一个数量级。
摘要：
图模式匹配是许多常见图挖掘任务所面临的一个基础性问题，也是若干图挖掘系统的基本构建模块。本文首次探索了如何利用查询模式和输入图的结构，主动对图进行剪枝，以加速图模式匹配。我们提出在查询执行期间构建辅助图，即图的不同剪枝版本。这需要在构建和管理辅助图的前期成本，与更快集合运算所带来的收益之间进行谨慎权衡。为此，我们提出了 GraphMini，这是一种新型系统，采用查询编译和新的成本模型，以最小化构建和维护辅助图的成本，并最大化收益。我们的评估表明，与常用基准上最先进的子图枚举系统相比，使用 GraphMini 可实现一个数量级的加速。
英文摘要：
Graph pattern matching is a fundamental problem encountered by many common graph mining tasks and the basic building block of several graph mining systems. This paper explores for the first time how to proactively prune graphs to speed up graph pattern matching by leveraging the structure of the query pattern and the input graph. We propose building auxiliary graphs, which are different pruned versions of the graph, during query execution. This requires careful balancing between the upfront cost of building and managing auxiliary graphs and the gains of faster set operations. To this end, we propose GraphMini, a new system that uses query compilation and a new cost model to minimize the cost of building and maintaining auxiliary graphs and maximize gains. Our evaluation shows that using GraphMini can achieve one order of magnitude speedup compared to state-of-the-art subgraph enumeration systems on commonly used benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
