Approximate Pattern Matching in Massive Graphs with Precision and Recall Guarantees

There are multiple situations where supporting approximation in graph pattern matching tasks is highly desirable: (i) the data acquisition process can be noisy; (ii) a user may only have an imprecise idea of the search query; and (iii) approximation can be used for high volume vertex labeling when extracting machine learning features from graph data. We present a new algorithmic pipeline for approximate matching that combines edit-distance based matching with systematic graph pruning. We formalize the problem as identifying all exact matches for up to k edit-distance subgraphs of a user-supplied template. We design a solution which exploits unique optimization opportunities within the design space, not explored previously. Our solution is (i) highly scalable, (ii) supports arbitrary patterns and edit-distance, (iii) offers 100% precision and 100% recall guarantees, and (vi) supports a set of popular data analysis scenarios. We demonstrate its advantages through an implementation that offers good strong and weak scaling on massive real-world (257 billion edges) and synthetic (1.1 trillion edges) labeled graphs, respectively, and when operating on a massive cluster (256 nodes/9,216 cores), orders of magnitude larger than previously used for similar problems. Empirical comparison with the state-of-the-art highlights the advantages of our solution when handling massive graphs and complex patterns.