An Efficient System for Subgraph Discovery

TL;DR

Nuri is a system that efficiently finds the top relevant subgraphs in large graphs using prioritized expansion and pruning, outperforming distributed systems in resource usage and speed.

Contribution

The paper introduces Nuri, a novel single-machine system for targeted subgraph discovery that reduces computational costs through prioritization and pruning strategies.

Findings

Nuri outperforms distributed systems by over 100x in speed.

Nuri efficiently handles large datasets on a single core.

Nuri is effective for clique discovery, subgraph isomorphism, and pattern mining.

Abstract

Subgraph discovery in a single data graph---finding subsets of vertices and edges satisfying a user-specified criteria---is an essential and general graph analytics operation with a wide spectrum of applications. Depending on the criteria, subgraphs of interest may correspond to cliques of friends in social networks, interconnected entities in RDF data, or frequent patterns in protein interaction networks to name a few. Existing systems usually examine a large number of subgraphs while employing many computers and often produce an enormous result set of subgraphs. How can we enable fast discovery of only the most relevant subgraphs while minimizing the computational requirements? We present Nuri, a general subgraph discovery system that allows users to succinctly specify subgraphs of interest and criteria for ranking them. Given such specifications, Nuri efficiently finds the k most relevant subgraphs using only a single computer. It prioritizes (i.e., expands earlier than others) subgraphs that are more likely to expand into the desired subgraphs (prioritized subgraph expansion) and proactively discards irrelevant subgraphs from which the desired subgraphs cannot be constructed (pruning). Nuri can also efficiently store and retrieve a large number of subgraphs on disk without being limited by the size of main memory. We demonstrate using both real and synthetic datasets that Nuri on a single core outperforms the closest alternative distributed system consuming 40 times more computational resources by more than 2 orders of magnitude for clique discovery and 1 order of magnitude for subgraph isomorphism and pattern mining.