Towards Practical and Robust Labeled Pattern Matching in Trillion-Edge Graphs.

Subgraph pattern matching is fundamental to graph analytics and has wide applications. Unfortunately, high computational complexity limits the robustness guarantees of existing algorithms: they do not scale for modern large graph datasets and/or they have limitations in terms of accuracy or in terms of the intricacy of the patterns supported. We present algorithms, theory, and empirical evidence that iteratively eliminating vertices that do not meet local constraints dramatically reduces the search space for pattern matching in real-world graphs, and demonstrate a scalable implementation of our algorithms. We additionally identify the characteristics of patterns for which every non-eliminated vertex participates in a match. These techniques are an essential step to enable scalable, practical solutions for robust pattern matching in large-scale labeled graphs.We demonstrate the advantages of the proposed approach through strong and weak scaling experiments on massive-scale real-world (up to 257 billion edges) and synthetic (up to 2.2 trillion edges) graphs and at scales (256 compute nodes with 6,144 processors) orders of magnitude larger than those used in the past for similar problems.